Airline revenue planning and forecasting system and method

ABSTRACT

A system and method for estimating airline demand includes (1) accessing capacity data for a previous N years at a Point of Sale (POS) level, time period level and an Origin and Destination (O&amp;D) level, (2) accessing flown data for a previous M years at the POS level, time period level, and O&amp;D level, (3) accessing capacity data for a forecasting period that extends beyond a time when reservation information is available (e.g., beyond twelve months), (4) calculating at least one of actual growth factor and market growth factor, (5) deriving an effective growth based on the flown data, the capacity data for the previous N years, the capacity data for the forecasting period and the at least one of the actual growth and the market growth, and (6) generating a passenger demand forecast for a budget year based on the effective growth. The time period level is any of daily, weekly, or monthly. The capacity data includes compartment level data. The flown data includes compartment level data. A set of weighting factors may be applied to the flown data and the market data to derive the at least one of actual growth and market growth. The weighting factors may include seasonality factors. Previous year&#39;s capacity is compared to budget year capacity. In one embodiment, N=M. In some cases, N=1. Average fares (yield) for the budget year are also estimated.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional PatentApplication No. 60/470,894, Filed: May 16, 2003, Titled: AIRLINE REVENUEPLANNING AND FORECASTING SYSTEM AND METHOD, and to U.S. ProvisionalPatent Application No. 60/471,146, Filed: May 17, 2003, Titled: AIRLINEREVENUE PLANNING AND FORECASTING SYSTEM AND METHOD, both of which areincorporated by reference herein.

BACKGROUND OF THE INVENTION

[0002] 1. Field of the Invention

[0003] The present invention relates to airline revenue planning, andmore particularly, to an airline revenue and yield forecasting andplanning system using linear programming techniques.

[0004] 2. Description of the Related Art

[0005] Revenue management systems seek to maximize the revenue generatedfrom a fixed service or productive capacity by selectively accepting ordenying requests for capacity. For example, airlines have a network offlights with a set of seats available for sale on a given day, andcustomers request seats in advance of travel for various itineraries onthe network. Based on the current reservations already accepted for eachflight (alternatively, on the remaining capacity available), the timeremaining in the sales horizon and forecasts of future demand foritineraries, airlines must decide which itineraries and fare classes toaccept, and which to deny (or close out).

[0006] These decisions are detailed and complex because future demand istypically uncertain, and one must evaluate complex tradeoffs between thecurrent and future value of capacity. Therefore, revenue managementdecisions are typically made, or guided by, a software system (revenuemanagement system or revenue planning system) that incorporates avariety of advanced statistical and mathematical methods. Revenuemanagement is widely used in the airline, hotel, car-rental, energy,natural gas pipelines, broadcasting, shipping, sports, entertainmentfacilities, manufacturing, equipment leasing and cargo industries.Indeed, the practice is applicable in any industry that has limitedshort-term capacity flexibility and variable demand.

[0007] A variety of mathematical models have been used to solve theproblem of deciding which requests to accept or deny based on currentcapacity and forecasts of future demand. However, regardless of themathematical model and assumptions used, revenue management softwaresystems ultimately need an internal control logic to implement theaccept/deny recommendations.

SUMMARY OF THE INVENTION

[0008] The present invention is directed to an airline revenue planningand forecasting system and method that substantially obviates one ormore of the problems and disadvantages of the related art.

[0009] In one aspect there is provided a system, computer programproduct and method of optimizing airline revenue that includes the stepsof accessing passenger and capacity constraints for a plurality of legsof a network, accessing fares for each leg, and performing anetwork-level linear optimization to derive a demand solution thatmaximizes network revenue.

[0010] The present invention also provides a system, computer programproduct and method for estimating airline demand including (1) accessingcapacity data for a previous N years at a Point of Sale (POS) level,time period level and an Origin and Destination (O&D) level, (2)accessing flown data for a previous M years at the POS level, timeperiod level, and O&D level, (3) accessing capacity data for aforecasting period that extends beyond twelve months, (4) calculating anactual growth factor and/or a market growth factor, (5) deriving aneffective growth based on the capacity data for the previous N years,the capacity data for the forecasting period and the actual growthand/or the market growth, and (6) generating a passenger demand forecastfor a budget year based on the effective growth. The time period levelmay be daily, weekly, or monthly. The capacity data can includecompartment level data. The flown data can include compartment leveldata. A set of weighting factors may be applied to the flown data andthe market data to derive the actual growth and/or market growth. Theweighting factors may include seasonality factors. Previous year'scapacity is compared to budget year capacity. In one embodiment, N=M. Insome cases, N=1. Average fares (yield) for the budget year are alsoestimated.

[0011] The present invention also provides a system, computer programproduct and method of setting sales targets for an airline that includes(1) estimating PAX demand and demand fares, (2) performing linearoptimization on a network level to maximize overall network revenuebased on the PAX demand and the demand fares and capacity constraints,and (3) generating PAX target and target fares for each POS for eachO&D, compartment and month based on the maximized network revenue.Target fares may be calculated based on fare type, such that the faretype includes one-way fares, return fares, excursion fares, three monthadvance fares, and six month advance fares. Target fares may becalculated based on market segment. The market segment includes touroperator, customer type, internet bookings, holiday travelers and/orfrequent flyers. Generating PAX target and target fares for each POS foreach O&D, compartment and month is based on the maximized networkrevenue and is done on a time period level. The time period level can bedaily, weekly or monthly. Generating PAX target and target fares takesinto account market segments (i.e., customer type, frequent flyer, touroperators, internet bookings, holiday travelers). PAX target and targetfares may be generated at a single travel agent level and/or at a salesexecutive/supervisor level. Targets may be generated based on a flightlevel (i.e., an itinerary level). The linear optimization may also takeseasonality into account, may balance inbound to outbound traffic.Industry travel demand may also be excluded from the optimization step.Sensitivity analysis may be performed to determine fares at whichrejected demand should be accepted. Additionally, in one embodiment,network revenue is unaffected by acceptance of rejected demand. Resultsof sensitivity analysis may be displayed, including rejected demand andminimum average fare for accepting the rejected demand.

[0012] The present invention also provides a system, computer programproduct and method of generating demand targets, including identifyingnetwork route demand, identifying currency value of the network routedemand, and deciding whether a POS should adopt a volume based on avalue-based strategy. Displaying routes of the network and color codingthem is based on the selected strategy. The routes may be superimposedon a map. The routes may be shown as a hub and spoke diagram. Onlyroutes of the network that account for at least X % of total networkrevenue could be displayed, if desired. The network may be a hub andspoke network, or a point-to-point network.

[0013] Additional features and advantages of the invention will be setforth in the description that follows. Yet further features andadvantages will be apparent to a person skilled in the art based on thedescription set forth herein or may be learned by practice of theinvention. The advantages of the invention will be realized and attainedby the structure particularly pointed out in the written description andclaims hereof as well as the appended drawings.

[0014] It is to be understood that both the foregoing generaldescription and the following detailed description are exemplary andexplanatory and are intended to provide further explanation of theinvention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in andconstitute a part of this specification, illustrate exemplaryembodiments of the invention and together with the description serve toexplain the principles of the invention. A list of abbreviations used indescribing the drawing is provided below in the Detailed Descriptionsection. In the drawings:

[0016]FIG. 1 shows building blocks of a revenue plan.

[0017]FIG. 2 shows a Revenue Plan Interface.

[0018]FIG. 3 shows a sample data granularity & span table used by aRevenue Planning System (RPS).

[0019]FIG. 4 shows an I-P-O (Input-Process-Output) of a DataSynchronization process.

[0020]FIG. 5 shows an I-P-O of Re-forecasting.

[0021]FIG. 6 shows an I-P-O of Near Period Re-forecasting.

[0022]FIG. 7 shows selected examples of special modeling cases.

[0023]FIG. 8 shows an I-P-O for Far Period Re-forecasting.

[0024]FIG. 9 shows special cases of when Passenger Growth and CapacityGrowth have contrasting indicators.

[0025]FIGS. 10 and 11A show principles of Yield Re-forecasting.

[0026]FIGS. 11B and 11C show Re-forecasting Summary reports.

[0027]FIGS. 12-15U show various reports related to Re-forecasting.

[0028]FIG. 16 shows PAX Re-forecasting constraining logic.

[0029]FIGS. 17-24 show comparative results for a Yield Re-forecast Modeland illustrate Yield Re-forecasting effectiveness measurement.

[0030]FIG. 25 shows an example of Re-forecast performance of a majorPOS.

[0031]FIGS. 26A-26H illustrate the process of using E-dialogue to arriveat a set of targets, and various features of E-dialogue functionality inthe process of setting up a Revenue Plan.

[0032]FIG. 27 shows an I-P-O for PAX Demand Estimation.

[0033]FIG. 28 shows a PAX Demand Estimation logic flow chart.

[0034]FIGS. 29A-29D are screen shots that illustrate details ofcalculating PAX demand.

[0035]FIG. 29E illustrates capacity highlights together with newdestinations.

[0036]FIG. 30 shows an example of a PAX Demand Estimation effectivenessgraph.

[0037]FIG. 31A shows an I-P-O for Yield Demand Estimation.

[0038]FIG. 31B shows a graph illustrating average fare (yield) growth.

[0039]FIG. 32 shows an example of a Yield Demand Estimation.

[0040]FIG. 33 shows an example of a Yield Demand Estimationeffectiveness graph.

[0041]FIG. 34 shows an example of a display of growth factors for allthe O&Ds for the POSs by region and month selected.

[0042]FIG. 35 shows an example of a Yield Growth report.

[0043]FIG. 36A shows an example of the Detail and Summary report withthe final demand for the months in cross tab fashion.

[0044]FIG. 36B summarizes the demand estimation process.

[0045]FIG. 37 shows a Linear Programming Optimization (LPO) ModelDerivation process.

[0046]FIG. 38 shows an I-P-O of an Optimization process.

[0047]FIG. 39 shows a Linear Programming Optimal Curve.

[0048]FIG. 40 shows an LPO (Linear Programming Optimizer) Model Tree.

[0049]FIG. 41 shows a sample airline route network.

[0050]FIG. 42A shows a Rejected Demand Report.

[0051]FIG. 42B summarizes the Optimization Process.

[0052]FIG. 43 shows a Pre-Optimization Process.

[0053]FIG. 44 shows a Post Optimization Process.

[0054]FIG. 45 shows a diagram of users of RPS output.

[0055]FIG. 46 shows a Revenue Plan Report.

[0056]FIG. 47 shows a Fully Rejected Demand Report.

[0057]FIG. 48 shows a Partially Accepted Demand Report.

[0058] FIGS. 49AA-49AB show a Regional Summary Report for PAX, yield andrevenue.

[0059]FIG. 49B shows a Regional Report for Europe and North Americaonly.

[0060]FIG. 49C shows a Network Summary Report for PAX, yield andrevenue.

[0061]FIGS. 49D-49E show a Commercial Target Report.

[0062]FIGS. 50A-51B illustrate additional aspects of the CommercialTarget Report.

[0063]FIGS. 52A and 52B shows a Commercial Target Report—Outstation.

[0064]FIG. 53 shows an O&D Capacity Comparison Report.

[0065]FIG. 54 shows a Sector Yield Report.

[0066]FIG. 55 shows a Quick Target Report.

[0067]FIG. 56 shows a POS Revenue Variance Report.

[0068]FIGS. 57 and 58 show the variance matrix in graphical form.

[0069]FIGS. 59A and 59B show a Route-wise and Yield and Seat Factor (SF)Report.

[0070]FIGS. 60A and 60B show a frequency distribution of fares ingraphical form.

[0071]FIGS. 61-64A illustrate fare type details for a single Point ofSale.

[0072]FIG. 64B summarizes a Core Market strategy selection process.

[0073]FIG. 65A shows a Revenue Plan Progress Report.

[0074]FIGS. 65B-65G show examples of monthly distribution reports.

[0075]FIG. 66 shows a Core Markets and New Markets entry screen.

[0076]FIG. 67 shows a POS summary report entry screen.

[0077]FIG. 68 shows an Outbound connections report.

[0078]FIG. 69 shows a Station Summary Report.

[0079]FIG. 70 shows a Core Market Strategy Report.

[0080]FIG. 71 shows a hub-and-spoke type Spider Web.

[0081]FIG. 72 shows a Spider Web superimposed on a map.

[0082]FIG. 73 shows a Route Demand Report.

[0083]FIG. 74 shows an Inbound Connection Report.

[0084]FIG. 75 shows an Integrated Revenue Plan.

DETAILED DESCRIPTION OF THE INVENTION

[0085] Reference will now be made in detail to the embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings.

TABLE OF CONTENTS

[0086] 1.0 Abbreviations

[0087] 2.0 Commercial Issues

[0088] 3.0 Introduction to Revenue Planning

[0089] 4.0 Revenue Plan Objectives

[0090] 5.0 Building Blocks of Revenue Plan

[0091] 6.0 Revenue plan interfaces

[0092] 7.0 Data Synchronization 101

[0093] 8.0 Re-forecasting Process 102

[0094] 8.1 Re-forecasting

[0095] 8.1.1 Re-forecasting PAX for months where forward bookings datais not available

[0096] 8.1.2 Re-forecasting Modeling approach

[0097] 8.1.3 Near Period Re-forecasting

[0098] 8.1.3.1 Multiplicative Model

[0099] 8.1.3.2 Pickup Model

[0100] 8.1.3.3 Multiplicative Model Simulation

[0101] 8.1.3.4 Pickup Model Simulation

[0102] 8.1.3.5 Model Validity Inference

[0103] 8.1.3.6 Special Cases Handling

[0104] 8.1.4 Far Period Re-forecasting

[0105] 8.1.4.1 Effective Growth Factor Model Simulation

[0106] 8.1.4.2 FP Model Inference

[0107] 8.1.4.3 Special Cases Handling

[0108] 8.2 Yield Re-forecasting

[0109] 8.2.1 Re-forecasting Yield Simulation

[0110] 8.2.2 Special Cases Handling

[0111] 8.2.3 Exception Reports for the Re-forecasted values

[0112] 8.2.3.1 Introduction

[0113] 8.2.3.2 POS Summary (PAX)

[0114] 8.2.3.3 POS Summary (Yield)

[0115] 8.2.3.4 Re-forecasted Data Update Facility

[0116] 8.2.3.5 Re-forecasted Data Reports

[0117] 8.2.4 Re-forecasting Model Inference

[0118] 8.3 Re-forecast PAX Constraining Logic

[0119] 8.3.1.1 Update of Re-forecasted PAX/Yield data into the Revenuedata

[0120] 8.4 Re-forecast Effectiveness Measurement

[0121] 9.0 Demand Estimation 103

[0122] 9.1 Introduction

[0123] 9.2 Demand Estimation Functional Process

[0124] 9.3 Derivation of Actual Traffic Growth Factor

[0125] 9.4 Derivation of Bookings Growth Factor from MIDT data

[0126] 9.5 Derivation of O&D Capacity Growth Factor

[0127] 9.6 Derivation of Effective Growth Factor

[0128] 9.7 Facility to Manually Edit and Store Effective Growth Factors

[0129] 9.8 Process to trigger the unconstraining of the baseline PAXdemand

[0130] 9.9 Demand Estimation for routes with less than one year flowndata

[0131] 9.10 Demand Estimation Derivation

[0132] 9.11 PAX Demand Estimation-additional factors

[0133] 9.11.1 Effective Growth Factor Derivation

[0134] 9.11.2 Weighted Passenger Growth Factor (WPGF)

[0135] 9.11.3 Weighted Market Share Factor (WMSF)

[0136] 9.11.4 Target Market Share (TMS)

[0137] 9.11.5 TMS Matrix

[0138] 9.11.6 Combined Traffic Growth (CTG)

[0139] 9.11.7 Capacity Growth Factor (CGF)

[0140] 9.11.8 Effective Growth Factor (EGF) Example

[0141] 9.11.9 PAX Demand Estimation —Sample Calculation

[0142] 9.12 Effectiveness of Passenger Demand Estimation

[0143] 9.13 Yield Demand Estimation

[0144] 9.13.1 Introduction

[0145] 9.13.2 Functional Requirements

[0146] 9.13.3 Derivation of Yield Growth Factor

[0147] 9.13.4 Process to trigger the unconstraining of the baselinedemand yield

[0148] 9.13.5 Yield Estimation for routes with less than one year flowndata

[0149] 9.13.6 Yield Demand Estimation —Sample Calculation

[0150] 9.14 Effectiveness of Yield Demand Estimation

[0151] 9.15 Reports For PAX demand and Yield Estimation

[0152] 9.15.1 Exception Reports for displaying the PAX Growth Factorsderived

[0153] 9.15.2 Exception Reports for displaying the Yield Growth Factorsderived

[0154] 9.15.3 The Final PAX demand and Yield after the unconstrainingprocess

[0155] 9.16 Summary of Demand Estimation

[0156] 9.17 Deriving a Model

[0157] 9.18 Linear Programming

[0158] 9.19 Optimizer Equations Example

[0159] 9.20 Seasonality

[0160] 9.21 Alignment of sales and revenue objectives

[0161] 9.22 Special Handling for “Industry Travel” Demand

[0162] 9.23 Balancing of Inbound/Outbound Traffic

[0163] 9.24 Sensitivity Analysis

[0164] 9.25 Summary of Optimization Process

[0165] 10.0 Pre-Optimization Processes

[0166] 10.1 Prorate Factor Generation 4301

[0167] 10.2 Sector-Route-Leg Link Generation 4302

[0168] 10.3 No Traffic Sector Nullification 4303

[0169] 10.4 Book Keeping Rate Update 4304

[0170] 11.0 Post Optimization Processes

[0171] 11.1 Sector Revenue Generation 4401

[0172] 11.2 Leg Seat Factor Generation 4402

[0173] 11.3 Sector-Route Revenue Generation 4403

[0174] 11.4 POS Revenue Variance Generation 4404

[0175] 12.0 Management Information System

[0176] 12.1 Reports

[0177] 12.1.1 Revenue Plan Report

[0178] 12.1.2 Fully Rejected Demand Report

[0179] 12.1.3 Partially Accepted Demand Report

[0180] 12.1.4 Commercial Target Report

[0181] 12.1.5 Commercial Target Report —Outstation

[0182] 12.1.6 O&D Capacity Comparison Report

[0183] 12.1.7 Sector Yield Report

[0184] 12.1.8 Leg Seat Factor Report

[0185] 12.1.9 Quick Target Report

[0186] 12.1.10 POS Revenue Variance Report

[0187] 12.1.11 Route-wise Yield and SF report

[0188] 12.1.12 Core Market Strategy Report

[0189] 12.1.13 Revenue Plan Progress Report

[0190] 12.1.14 Threats/Opportunities

[0191] 13.0 Additional Enhancements

[0192] 13.1 Core and New Markets

[0193] 13.2 POS Summary Report

[0194] 13.2.1 Overview of POS Summary

[0195] 13.2.2 Station Objectives

[0196] 14.0 Target Pack

[0197] 14.1 Commercial Target Outstations Report

[0198] 14.2 Station Summary Report

[0199] 14.3 Core Market Strategy Report

[0200] 14.4 Spider Web

[0201] 14.5 Route Demand Report

[0202] 14.6 Connection Reports

[0203] 15.0 Additional Features of Revenue Plan

[0204] 16.0 Advantages of the invention

[0205] 17.0 Conclusion

[0206] 1.0 Abbreviations

[0207] In the description that follows, the following abbreviations areused: Act Actual AOS Area of Sale ASKM Available Seat Kilometer BOMBombay CAM Commercial Analysis Manager CGF Capacity Growth Factor CompCompartment (i.e., Economy, Business class, First class) Cpn Coupons CTGCombined Traffic Growth CVIEW Corporate View Software DXB Dubai EDFEffective Demand Factor EGF Effective Growth Factor EGFM EffectiveGrowth Factor Model FBLY Forward Booking Last Year FBTY Forward BookingThis Year FCLY Flown coupon Last Year Fcst Forecast Flwn Flown CouponsGCC Gulf Cooperation Council I-P-O Input-Process-Output JKT Jakarta,Indonesia LGW London Gatwick LHR London Heathrow LHRDXB London Heathrowto Dubai Lyr Last Year MEA Middle East MEL Melbourne MF MaterializationFactor MIDI Market Intelligence Data Tape O&D Origin and Destination PAXPassenger PER Perth (Australia) PGF Passenger Growth Factor POS Point ofSale PROMIS Passenger Revenue Optimization Management Information SystemRev Revenue RPKM Revenue Passenger Kilometer RPS Revenue Planning SystemSIN Singapore SF Seat factor SYD Sydney TBK Total Booking TBK Lyr TotalBooking Last Year Tgt Target TMS Target Market Share Var Variance WAPRWest Asia/Pacific Rim WMGF Weighted Market Growth Factor WPGF WeightedPassenger Growth Factor YLD Yield YTD Year-To-Date

[0208] 2.0 Commercial Issues

[0209] In order to be successful, an airline needs to define “where itis going” (its strategic objectives), develop a revenue plan to “getthere” (how to achieve the objectives) and then align commercialoperations to deliver the revenue plan.

[0210] The present invention relates to an integrated platform toimprove an airline's Revenue Planning Process and align sales efforts tocorporate objectives/strategies. A Revenue Planning System (RPS)generates an Origin and Destination-based revenue plan for the airlineby scientifically creating revenue targets that are aligned tocommercial objectives, and optimized to ensure the best traffic mix.Once the revenue plan is created, optimized and published, the RevenuePlanning System helps the airline align its ongoing sales efforts to therevenue plan by tracking and reporting performance against targets usingan integrated performance monitoring toolkit.

[0211] Business objectives defined and met by the present inventioninclude the following:

[0212] Translating commercial objectives of an airline into a revenueplan based on scientific principles;

[0213] Optimizing the revenue plan and formulating the most profitabletraffic mix for the budget year;

[0214] Identifying potential routes/areas of sale for the airline thatwill yield significant commercial benefits;

[0215] Establishing market share targets for the budget year;

[0216] Publishing revenue budget packs for sales at an Origin andDestination (O&D), Point of Sale (POS) and Compartment (Comp) level; and

[0217] Facilitating monitoring of actual performance against revenueplan/targets.

[0218] The business function of the invention is therefore to provide ascientific Revenue Planning System that facilitates creation ofoptimized sales targets. Some of the features of the RPS are as follows:

[0219] Comprehensive Re-forecasting process to refine passenger (PAX)and yield forecasts for the baseline year;

[0220] Multiple mathematical models for near and far term forecasting;

[0221] Multiple mathematical models for early and late booking markets;

[0222] Tuning of Re-forecasts based on capacity constraints;

[0223] Weighted average passenger, capacity and market share growthfactors to build demand estimation from the Re-forecasted baseline;

[0224] Linear programming-driven optimizer based on specializedequations;

[0225] Network optimization based on demand estimates, yield estimatesand scheduled capacity constraints;

[0226] Generation of Point of Sale (POS), Origin and Destination (O&D),Month and Compartment level optimized targets;

[0227] Support for collaborative work and agreement on targets acrossmultiple organizational entities within an airline;

[0228] Support for distribution of budget packs that include targets andrelevant management reports for the global sales community; and

[0229] Detailed MIS on the revenue plan as well as a monitoring toolwhich facilitates comparison of actuals against targets.

[0230] The RPS assures efficient and effective measurable sales targets.It acts as a foundation to formulate the commercial objectives, andhelps the sales community to have a focused approach in day to daybusiness. The Revenue Planning Process helps meet the growing challengesin the area of revenue generation. As the airline business is highlycompetitive and volatile, it is important to profitability to have asystem to project the right traffic mix.

[0231] 3.0 Introduction to Revenue Planning

[0232] Revenue planning comprises a number of interdependent cohesiveprocesses that are developed based on an extensive study done in thefield of optimization and forecasting models. The RPS is adecision-making system with built-in intelligence to project the righttraffic mix that will be beneficial for an airline. The RPS identifiesthe market demand that is realistic and achievable. The RPS ispreferably based on the Linear Programming (LP) methodology, where itoptimizes the traffic mix based on the capacity, fare and demandconstraints existing in different routes. The RPS enables an airline totake full advantage of its available information, thereby maximizingbenefits, capitalizing on opportunities and gaining competitiveadvantage. The RPS is aligned with market conditions and fare structureto maximize revenue.

[0233] The RPS generates an Origin and Destination-based revenue planfor the airline by generating scientifically based revenue targetsaligned to commercial objectives and optimized to ensure the besttraffic mix for the budget year. The RPS helps the airline align itsongoing sales efforts to the revenue plan by tracking and reportingperformance against targets using an integrated performance monitoringtoolkit.

[0234] 4.0 Revenue Plan Objectives

[0235] The objectives of the Revenue Planning Process are:

[0236] To formulate commercial objectives;

[0237] To formulate the optimal traffic mix for budget year;

[0238] To identify the potential routes/area of sale for the airline'scommercial benefits; and

[0239] To establish a market share target for the budget year.

[0240] 5.0 Building Blocks of Revenue Plan

[0241] As shown in FIG. 1, the Revenue Planning Process includes severalclosely linked processes (building blocks). These processes include adata synchronization process 101, which synchronizes flown data withCVIEW data (or another source of market data). Re-forecasting of (PAXand Yield) 102 for future months builds a base for future months demandestimation. Demand Estimation 103 estimates demand for the budget year.A Fine Tuning Process 104 identifies peaks and valleys in the demanddata patterns. An Optimization Process 105 applies demand and capacityconstraints to the problem of optimizing traffic mix. Target Review 106allows area managers to provide input into the target setting process.Target Finalization 107 includes feedback from the area managers. ADistribution step 108 is where revenue targets are sent to each Area ofSale. These processes may be implemented in modular form, such that eachof the steps 101-108 is a separate module.

[0242] Each process 101-108 is tightly coupled and influences subsequentprocess performance. One process abnormality/error can cause rippleeffects in subsequent processes. At the end of each process, a go/no-godecision is made on whether or not the subsequent process can proceed.

[0243] 6.0 Revenue Plan Interfaces

[0244]FIG. 2 illustrates the RPS 100 interfaces, such that the RPS 100can access the various data from the data sources. In one embodiment,the RPS 100 receives the flown data (Passenger, Yield, Revenue) fromCVIEW 201 and receives market share data from MIDT 202. A PlanningSystem 204 feeds the budget year scheduled capacity to the RPS 100, andPROMIS 203 feeds the operational capacities of current financial yearand previous year to the RPS 100. The output of the RPS 100 is a targetpack 205, which is sent to each area of sales 206. An RPS database 207is used to store various RPS-related parameters and data.

[0245] A data granularity & span table in FIG. 3 gives an example ofdata received from different systems for the Revenue Planning System 100processes. For example, as shown in FIG. 3, CVIEW 201 provides thefollowing data to the RPS 100: advance bookings, total bookings for lastyear, flown data, actual yield (local currency), actual yield (airlinesbased currency), actual revenue (local currency), and actual revenue(airlines base currency). All of the data from CVIEW 201 is providedwith a level of granularity of POS-O&D-Comp-Travel Month (in otherwords, the data is by provided by POS and by O&D and by compartment andby travel month). As illustrated in FIG. 3, PROMIS 203 provides capacitydata, which is provided at the level of granularity of Leg-Comp-TravelMonth. The Planning System 204 provides capacity planning data at theLeg-Comp-Travel Month level of granularity and so forth.

[0246] 7.0 Data Synchronization 101

[0247] Before a start of any process 101-108, actual flown data(Revenue, PAX, Yield) from CVIEW 201 is loaded for flown travel monthsof the current budget year. This forms the base for Demand Estimation103 of the same months in the next budget year. For example, it is donefor April 2002-August 2002 travel months at the time of Revenue PlanningProcess, and this data forms the base for Demand Estimation 103 of April2003-August 2003 of the next budget year (in this example, 2003).

[0248] This is also illustrated in FIG. 4, which shows the I-P-O of thedata synchronization process 101. The data synchronization process 101takes as input 401 actual PAX, actual revenue and actual yield. As shownat block 402, processing involves synchronizing RPS 100 data with CVIEW201 data. The output 403 forms a base for projecting PAX demand formonths from April through the month at the time of the revenue PlanningProcess, in this example. The output of each process forms an input tosubsequent process. Each process plays a role in producing a successful,reliable, accurate and practical Revenue Plan.

[0249] 8.0 Re-Forecasting Process 102

[0250] Subsequent to data synchronization with flown data from CVIEW 201for the months of April 2002-August 2002, Re-forecasting 102 is carriedout to estimate the passenger and yield for the remaining months (i.e.,September 2002-March 2003) for the current financial year.

[0251] Target setting is done at the O&D and Compartment level for allthe POSs across all Regions. The components that are manipulated toderive the target revenue are the PAX target and the yield (yielddefined as average fare). The baseline for deriving the PAX target andyield for the target year are the flown PAX data from the months of thecurrent financial year.

[0252] For all the months where Re-forecasting 102 is to be carried out,the target values of the current year acts as the initial baseline flowndata. This data becomes the ‘Actual PAX and Yield and Revenue’ data. TheRe-forecasting process 102 derives the forecasts, which replace thesebaseline values after review and confirmation by the users. Oncompletion of the Re-forecasting process, the baseline for the targetsetting process (107-108 in FIG. 1) is ready, i.e., all the months forthe current financial year have the flown information (actual flownvalues for the months where flown data is available and the forecastedflown values—through re-forecasting—for months where flown informationis not available).

[0253] The Re-forecasting Process 102 is then carried out for derivingthe forecasts for PAX (503) and Yield (504) values.

[0254] 8.1 Re-Forecasting

[0255] As noted above, Re-forecasting 102 is applied to PAX (503) andyield (504), as shown in FIG. 5. PAX Re-forecasting 503 involvesestimating expected coupons for each targeted POS-O&D combinations foreach compartment for the current financial year for remaining months.This forecast data forms the base for estimating PAX demand for the nextfinancial year for same months. FIG. 5 shows the I-P-O diagram for PAXRe-forecasting 503.

[0256] The objective is therefore to derive the estimated flown PAX forthe months where the actual flown data has not been available, i.e., thefuture months of the current financial year where travel is yet to bemade. The PAX figures are derived at the O&D and Compartment level forthese months for all the POSs.

[0257] The inputs 501 (see FIG. 5) into PAX Re-forecasting 503 areActual data for pervious/current year, POS Growth, capacity growth data,and advance booking data.

[0258] Advance bookings data is available from a commercial database atthe Monthly and POS and O&D and compartment level for the latestsnapshot date. This advance bookings data is available for the next sixmonths from the latest snapshot date.

[0259] Advance bookings data for these months is available from theprevious year at the Monthly and POS and O&D and compartment level.Flown PAX information for these months is available from the previousyear at the Monthly and POS and O&D and compartment level.

[0260] The PAX Re-forecasting process 503 then derives the forecasts(PAX) for the applicable months (see output 502 in FIG. 5). This becomesthe base for projecting PAX demand for the budget year for the samemonths.

[0261] PAX Re-forecasting 503 may be done using forward bookings, or itmay be done without forward bookings. A process is therefore needed thatderives the forecasts for months where forward booking data is notavailable in the commercial database. This process uses the forwardbooking data to generate the forecast or the estimated flown PAX. Theuser can select the number of months (in one embodiment, not to exceedsix, although it may be more or less than six) that the forward bookingdata should be used for the forecast generation and this should beparameterized. By default, six months forward booking data will be usedfor the forecast generation. The revenue data table is the driving tableat the Month and Comp and POS and O&D level. A system parameter recordsthe months in the revenue data that is in need of the Re-forecastedvalues for baselines. For each O&D picked for the months where PAXRe-forecasting is necessary, the forecasts are calculated. The formulaused for the forecasting is governed by the following:

[0262] For each O&D under each POS and for each Month and for everycompartment—the following conditions are checked, and the ensuingforecast formula is applied to derive the forecasted PAX:

[0263] FBTY—Forward Bookings This year

[0264] FBLY—Forward Bookings Last Year

[0265] FCLY—Flown PAX Last Year

[0266] If {(FBTY>100 and the FBLY>100) and ((FBTY/FBLY)<3) and((FCLY/FBLY)<3)} is TRUE then the Linear Forecast Model is used forforecasting the PAX

Forecasted PAX={(FBTY*FCLY)/(FBLY)}*POS Forecast error=>Linear ForecastModel

[0267] Else the Zero Booking Model is used

Forecasted PAX={(FBTY+FCLY)−(FBLY)}*POS forecast error--=>Zero BookingModel

[0268] POS Forecast error values are given by the users in aspreadsheet, and may be loaded into the RPS database 207. Here, the ZeroBooking Model refers to a month (for example, a month 11 months fromnow), for which there are, at this point in time, no tickets purchasedyet.

[0269] The forward booking data is picked up based on the snapshot datefor the current year and the same date from the previous year. Theprocess preferably checks for the availability of the forward bookingdata of the specified snapshot dates in the commercial database. If thesnapshot date data is not available in the current or previous year, theprocess will display the error message, and the system parameter dateshould be changed for the date the data is available. (Any snapshot datein August will contain the forward booking data for the next six months,e.g., September to February).

[0270] The Re-forecasted values for PAX may be stored external to therevenue data table. The entities that need to be stored are, forexample: YearMonth, Region, POS, O&D, Compartment, Currency, BaselinePAX, and Re-forecasted PAX. The baseline PAX can be populated with thevalues in the revenue data that have been made in the baseline, in theabsence of the flown data.

8.1.1 Re-Forecasting PAX for Months Where Forward Bookings Data is notAvailable

[0271] Typically, when the PAX Re-forecasting process 503 is beingcarried out in August of the current year, the forward booking data willbe available for the next six months. In this case, for March 2003, theforward bookings data will normally not be available.

[0272] To derive the Re-forecasted PAX for March 2003, the flowninformation for March 2002 is taken from a commercial database. Theyear-over-year growth of the flown PAX for the months March 2001 andMarch 2002 is derived.

[0273] The capacity growth between the March 2002 and March 2003 is alsoderived. The capacity is stored in the RPS database 207 at the O&D andCompartment and Year and Month level, and capacity growth can becalculated. Data for both the current and the target year is maintained.

[0274] The Effective Growth Factor (flown PAX or the Capacity GrowthFactor), which will be used to derive the re-forecasted data, is basedon the following condition: If Flown PAX Growth Factor> Capacity GrowthFactor then Effective Growth Factor = Flown PAX Growth Factor Else ifflown PAX Growth Factor < Capacity Growth Factor then Effective GrowthFactor = Average (Flown PAX Growth Factor, Capacity Growth Factor).

[0275] The Effective Growth Factor is applied on the March 2002 flownPAX data from the commercial database, and the Re-forecasted data forMarch 2003 are obtained. It is also moved across to the Re-forecast datastore. This process is preferably run at the beginning of the entiretarget setting process. This ensures that there are no new O&Ds in thesystem, which do not have a baseline value.

[0276] The process records details in a log, including the following:

[0277] 1. Start date & time of process,

[0278] 2. User id,

[0279] 3. Parameter details,

[0280] 4. Snapshot date which was used for picking up the forwardbooking data of this year and last year, and

[0281] 5. POS-wise Revenue data baseline PAX totals (updated to the RPSdatabase 207).

[0282] 8.1.2 Re-Forecasting Modeling Approach

[0283] For example, at the time of revenue planning, the start of thenext budget year may be six months away. It requires expectedperformance of remaining months in the current budget year, which formthe base for the Demand Estimation 103 of the next budget year. Accuracyof this base data will play a major role in accurately predicting thedemand for the budget year.

[0284] In order to forecast the PAX demand, the current booking thateach POS-O&D achieved at the time of the Re-forecast, and their expectedutilization/cancellation rates, are used as a starting point. In orderto project the utilization/cancellation rates of POS-O&D-Compcombinations for a particular future travel month, the Revenue PlanningSystem 100 calls for a comparative analysis based on the actual data ofthe same flown months in the past. In one embodiment, CVIEW 201 does nothave the comparative Forward Booking information for travel monthsbeyond three months. Hence, it is not possible to forecast the expectedcoupons for travel months beyond three months. In order to overcomethis, two models have been derived to forecast PAX, as discussed below(although it will be understood that the invention is not limited tothese models):

[0285] Near Period forecasting (for example, forecasting PAX for thenext four months, e.g., the months September 2002, October 2002,November 2002, December 2002); and

[0286] Far Period forecasting (for example, forecasting PAX for thethree months after December 2002, e.g., January 2003, February 2003,March 2003).

[0287] 8.1.3 Near Period Re-Forecasting

[0288]FIG. 6 shows the I-P-O diagram for Near Period Re-forecasting. Asshown in FIG. 6, a Near Period Re-forecasting process 602 may use aPickup Model, or a Multiplicative Model, discussed below. Inputs to theNear Period Re-forecasting process 602 are total bookings, totalbookings last year, flown coupons last year, capacity, and advancedbookings for the month. The output 603 of the Near Period Re-forecastingprocess 602 is an unconstrained PAX forecast.

[0289] As further shown in the I-P-O diagram of FIG. 7, total bookingslast year (Lyr), and flown coupon Lyr are used to determine amaterialization factor (MF) of a given POS-O&D-Compartment combination.As PAX Re-forecasting 503 is usually done on a monthly basis, samemonth's but last year's data is used to determine the materializationfactor.

[0290] After a detailed analysis of booking materialization and trendanalysis, two Near Period methods were selected by the inventors, asnoted above, which empirically proved to be optimal forecasting models,by keeping in mind the type of booking patterns expected from differentmarkets. Two examples of Near Period Re-forecast models for PAXRe-forecasting are, Multiplicative Model for early booking markets, andPickup Model for late bookings markets.

8.1.3.1 Multiplicative Model

[0291] The Multiplicative Model is typically used in early bookingmarkets, where materialization of booking is assumed to have a linearrelationship with the Total booking that each POS holds for particularO&D for a given compartment for a given travel month. Boundaryconditions are set for this model to take care of exceptional bookinggrowth and materialization.

[0292] In order to limit the exaggeration in forecasting, certainboundary conditions have been arrived at after empirical experiments.The Multiplicative Model and the assumed boundary conditions are givenbelow:

Forecast=MF*TBK(Total Booking)

[0293] Where:

[0294] MF (Materialization factor)=FCLY/TBLY

[0295] FCLY=Flown Coupon Last Year

[0296] TBLY=Total Booking Last Year

[0297] TBK=Total Booking

[0298] Boundary Conditions:

[0299] (I) Total Booking<3*Total Booking Last year

[0300] (II) Flown coupon Lyr<3*Total Booking Last year.

[0301] 8.1.3.2 Pickup Model

[0302] The Pickup Model is used whenever any POS-O&D advance bookingdata does not meet the boundary conditions of the Multiplicative Model,typically in late booking markets. The Pickup Model's formula is shownbelow.

Forecast=(FCLY−TBLY+TBK)*PGF

[0303] Where:

[0304] FCLY=Flown Coupon Last Year

[0305] TBLY=Total Booking Last Year

[0306] TBK=Total Booking

[0307] PGF=POS Growth Factor

[0308] Boundary conditions: TBK/TBLY<3 and FCLY/TBLY<3.

[0309] This model is also used as a Zero Booking Model.

8.1.3.3 Multiplicative Model Simulation

[0310] The following simulation was done for the Multiplicative Model:Simulation Parameters SS Date: 01 May POS: UAE (DUBAI) OD: LHRDXB Comp:Y Travel Month: Jul 02 ACTUAL FLOWN TBK TBLY TARGET COUPONS FCLY 444 4231,214 1,227 1,103

[0311] The multiplicative model simulation example above uses thefollowing parameters: date: May 1, travel month: July 2002, POS: DXB,O&D LHRDXB, Comp: Y, TBK: 444, TBLY: 423, Target: 1214, Actual Flown:1227, and FCLY: 1103. The simulation results are as follows:

[0312] Boundary Conditions (I) TBK/TBLY=444/423=1.05<3

[0313]  (II) FCLY/TBLY=1,103/423=2.6<3

[0314] Since the boundary conditions are satisfied, the MultiplicativeModel is used in this case. This model yields: $\begin{matrix}{{{Materialization}\quad {Factor}\quad ({MF})} = {{FCLY}/{TBLY}}} \\{{~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}{= {1,{109/423}}}} \\{{= 2.62}} \\{{Forecast}{\quad } = {{MF}*{TBK}}} \\{{= {2.62*444}}} \\{{Actual}{\quad } = {1,227}} \\{{{Forecast}\quad \% \quad {Var}}{\quad } = {{\left( {{1,164} - {1,227}} \right)/1},227}} \\{{{Forecast}\quad {Error}}{\quad } = {{- 5.13}\%}}\end{matrix}$

8.1.3.4 Pickup Model Simulation

[0315] The following simulation example was done for the Pickup Model:Simulation Parameters SS Date: 07 April POS: UAE (DUBAI) OD: LHRDXBComp: Y : Travel Month Jul 02 ACTUAL FLOWN TBK TBLY TARGET COUPONS FCLY252 253 1,214 1,227 1,103 Boundary Conditions: (I) TBK/TBLY = 252/253 =0.99 < 3 (II) FCLY/TBLY = 1,103/269 =   4.35

3

[0316] In this case, the second boundary condition doesn't hold true.Hence, the Revenue Planning System 100 selects the Pickup Model. Pointof Sale Growth Factor derivation shown in the table below. Jul-97 Jul-98Jul-99 Jul-00 Jul-01 Flown 1,161 1,025 1,339 966 1,103 Growth Factor 9%−12%  31%  −28%  14%  Weights 0.05 0.15 0.20 0.25 0.35

[0317] In this example: POS: DXB, O&D: LHRDXB, Comp: Economy, TravelMonth: July 2003 $\begin{matrix}{{WPGF}{~~~~~~~~~~~~~~~~~~~} = {{0.05*9} +}} \\{{~~~~~~~}{{0.15*\left( {- 12} \right)} +}} \\{{~~~~~~~}{{0.20*31} +}} \\{{~~~~~~~}{{0.25*\left( {- 28} \right)} +}} \\{{~~~~~~~}{{0.35*14} +}} \\{{= {2.75\%}}} \\{{Forecast}{\quad } = {{FCLY} - {TBLY} + {TBK}}} \\{{= {{1,103} - 253 + 252}}} \\{{= {1,102}}} \\{{= {1,102\quad \left( {{Actual} = {1,227}} \right)}}} \\{{{Forecast}\quad \% \quad {Var}} = {{\left( {{1,102} - {1,227}} \right)/1},227}} \\{{{Forecast}\quad {Error}}{\quad } = {{- 10}\%}}\end{matrix}$

8.1.3.5 Model Validity Inference

[0318] It should be noted that for the same entity (DXB, LHRDXB Sector,July 2002 travel month, Y compartment), the two different models wereused on different snapshots (i.e., sets of data for a particular date).Thus, for 7 April snapshot, the Pickup Model was used, and for 1 Maysnapshot, the Multiplicative model was used. Both gave forecasts thatwere well within the expected range. Depending on the booking growththat a particular POS holds, suitable models can be automatically usedto predict the expected passenger demand with minimum forecast errors.

8.1.3.6 Special Cases Handling

[0319] Some examples of special cases, where input conditions arechecked to ensure that suitable models are chosen for the Re-forecastingProcess 102 (see also section 8.1.4.3 below), are illustrated in FIG. 7.The RPS 100 checks for selection of appropriate forecasting model basedon Total bookings, Total bookings Last year and Flown. The table in FIG.7 gives examples of forecast model selection based on input conditions.

[0320] 8.1.4 Far Period Re-Forecasting

[0321] PAX Re-forecasting 503 for the month where forward booking datais not available, is carried out with the help of the POS Growth Factorand the Capacity Growth Factor. Where PAX Re-forecasting 503 is carriedout in the month of September 2002, comparison of forward booking datawith last year is available only for the next three months, i.e.,October 2002, November 2002, December 2002. For January 2003, February2003, March 2003, advance booking data will not have last year bookingdetails, hence it is not possible to use the models. It is thennecessary to use another model called the Effective Growth Factor Model(EGFM). The Input-Process-Output (I-P-O) diagram of FIG. 8 shows detailsof Far Period forecasting.

[0322] As shown in FIG. 8, the process of Far Period Re-forecasting usesan Effective Growth Factor Forecast Model 802. Its inputs 801 are flowncoupons for last year, flown coupons last last year (i.e., the yearbefore last year) and capacity of current year. The output 803 of themodel is an unconstrained PAX forecast.

[0323] Thus, PAX growth (PG) is calculated for thePOS-O&D-Compartment-Year month combination by looking at the actual datafor last year and the year before year. For example to forecast the PAXfor March 2003, March 2002 and March 2001 actual flown data is used toget the Passenger Growth Factor, and the Capacities for March 2002 andMarch 2003 are considered to calculate the Capacity Growth Factor. Afterobtaining these two factors, the Effective Growth Factor is derived.

[0324] The Effective Growth Factor Model is shown below: If PGF >0 & CGF > 0 &  PGF > CGF then EGF = PGF Else EGF =(PGF + CGF)/2 Forecast= EGF * Flown Coupon Last Year

[0325] Where:

[0326] EGF=Effective Growth Factor;

[0327] PGF=Passenger Growth Factor=(Flown Coupons₀₂−FlownCoupons₀₁)*100/Flown Coupons₁₀;

[0328] CGF=Capacity GrowthFactor=(Capacity₀₃−Capacity₀₂)*100/Capacity₀₂;

[0329] Boundary conditions: PGF Upper Limit=50%, and PGF LowerLimit−30%. These boundary conditions are used to remove data outliers.The forecast is then derived from the EGF as follows:

[0330] Forecast=EGF*Flown coupons Last Year.

8.1.4.1 Effective Growth Factor Model Simulation

[0331] In the EGFM simulation example below: Simulation Parameters SSDate: 01 March POS: UAE (DUBAI) OD: LHRDXB Comp: Y Travel Month Jul 02TBK Flown ₀₁ Flown ₀₀ Cap ₀₂ Cap ₀₁ 103 1,103 966 24,021 24,534 PGF =(Flown ₀₁ − Flown ₀₀)/Flown ₀₀ * 100 = (1,103 − 966)/966* 100 = 14% CGF= (Cap ₀₂ − Cap ₀₁)/Cap ₀₁ * 100 = (24,021 − 24,534)/24,534 * 100 = −2%

[0332] In this case, PGF>0 and CGF<0, hence, EGF will be calculated asshown below: $\begin{matrix}{{EGF} = {\left( {{PGF} + {CGF}} \right)/2}} \\{{EGF} = {\left( {14 - 2} \right)/2}} \\{{= {6\%}}}\end{matrix}$

[0333] Therefore: $\begin{matrix}{{Forecast}{\quad } = {{EGF}*{Flown}_{01}}} \\{{= {1.06*1,103}}} \\{{= {1,169}}} \\{{Actual}{\quad } = {1,227}} \\{{{Forecast}\quad \% \quad {Var}} = {{\left( {{1,169} - {1,227}} \right)/1},227}} \\{{{Forecast}\quad {Error}}{\quad } = {4.7\%}}\end{matrix}$

8.1.4.2 FP Model Inference

[0334] Experimental results show that in the Far Period, forecast erroris well below 5%. This model was tried in other cases, and was found tobe successful. From a simulation, it was shown that for the Late BookingMarket, the RPS 100 takes the Pickup Model during the initial snapshots.When it approaches the travel month, the RPS 100 considers theMultiplicative Model. However, in the Early Booking Market, a majorityof the time the RPS 100 uses the Multiplicative Model.

[0335] Of the two forecasting methodologies discussed above, the FarPeriod Method and the Near Period Method have shown consistent forecasterrors at varying snapshots. Therefore, these can be considered assuitable to any type of booking conditions.

8.1.4.3 Special Cases Handling

[0336] A thorough checking should preferably be done for some specialcases.

[0337] Case I: When PG (Passenger Growth) and CG (Capacity Growth) havecontrasting indicators, as shown in the table of FIG. 9.

[0338] Case II: Offline Points have become Online Points, e.g.,Mauritius, Australia (Western), Japan (Eastern) and India-Hyderabad. Forthese POS, the Re-forecast PAX is same as PAX target for the currentbudget year.

[0339] 8.2 Yield Re-Forecasting

[0340] The Yield Re-forecasting process 504 estimates the expectedaverage fare (Yield) for each of the targeted POS-O&D combinations foreach Compartment for the remaining months of the current financial year.This data forms the basis for estimating the fare demand for the samemonths for the next budget year.

[0341] The objective of the Yield Re-forecasting process 504 is toderive the estimated yield for the months where the actual flown datahas not been received and for the future months of the current financialyear where travel is yet to be made. The yield figures are derived atthe O&D and Compartment level for these months for all the POSs.

[0342] The input is YTD yield variance of the available flown data withregard to the targets for the current year from the commercial databaseat the POS and O&D and Comp level. The Yield Re-forecasting process 504derives the yield forecasts for the applicable months.

[0343] YTD yield variance for the flown data is taken from thecommercial database. The yield variance with regard to the targets isobtained at the POS and O&D and Comp level.

[0344] A set of parameters called “capping factors” are used, and arecalled Upper and Lower limits.

[0345] The YTD yield variance (in %) for each Comp and POS and O&Dcombination is compared against these limits, and, if it fits within theband, then is applied against the baseline yield value of the POS andO&D for the month where Re-forecasting is required. After the adjustmentfactor is applied to the yield (i.e., the baseline yield is increased ordecreased by this % value), the yield values are moved to theRe-forecast data store. If the YTD yield variance % value is beyond thecapping band, then the Lower or Upper limit will be factored into thebaseline (i.e., if the YTD variance % was below the lower band then theLower limit value is used—similarly, if the Upper limit is crossed, thenthe Upper limit value is used).

[0346] The parameters that are normally used for the YieldRe-forecasting 504 are YearMonth, Region, POS, O&D, Compartment,Currency, Baseline Yield (Local Currency), Baseline Yield (in baselinecurrency), Re-forecasted Yield (Local Currency), and Re-forecasted Yield(in baseline currency).

[0347] In the Yield Re-forecasting process 504, year-to-date actualyield and year-to-date target yield are considered in local currency(instead of, for example, the airline's base currency). This is done toreflect the real variation in yield including the fluctuations in thecurrency value. Once YTD values are obtained, percent variation isobtained and it is applied on the base yield of future month. In thiscase, base yield is the Target Yield for the current budget year.

[0348] The values are in Local Currency and/or the (baseline currency)are the values in baseline currency computed using the exchange rates inthe system. There is a system parameter called the base bookkeepingmonth, and the exchange rates pertaining to that month is picked up forcomputing the conversion to the baseline currency.

[0349] The baseline yield may be populated with the values in therevenue data which have been made the baseline in the absence of theflown data.

[0350] It is preferred that this process performed before the new targetsetting processes 106-108. This ensures that there are no new O&Ds inthe system that do not have a baseline value.

[0351] The process records details in a log, including the following:

[0352] 1. Start date & time of process

[0353] 2. User id

[0354] 3. Parameter details

[0355] 4. Snapshot date which was used for picking up the forwardbooking data of this year and last year

[0356] 5. POS-wise revenue data baseline average yield in baselinecurrency and Local Currency (updated to the RPS database 207).

[0357]FIG. 10 shows an I-P-O diagram of Yield Re-forecasting. As shownin FIG. 10, the process of Yield Re-forecasting 504 uses as inputs 1001actual year-to-date yield, target year-to-date yield, and current targetyield. The output of the Yield Re-forecasting process 504 is there-forecasted yield 1003.

[0358]FIG. 11A shows the Re-forecasting Process 102 in flowchart form.This process estimates the expected average fare (yield) for eachtargeted POS-O&D combinations for each compartment for the currentfinancial year for the remaining months. This forecast data forms thebasis for estimating demand fare for the next budget year for the samemonths.

[0359] As further illustrated in the flowchart of FIG. 11A, theRe-forecasting process 102 starts with a set of revenue data (step1101). The next step involves Yield Re-forecasting (step 504), PAXRe-forecasting (step 503), and Re-forecasting both PAX and Yield for themonths where forward booking is not available (step 1104). Followingsteps 1103 and 1104, a set of re-forecasted data 502 is created (step1105). After that, exception reports are generated, and input forms areupdated (step 1107). The Re-forecasting process 102 may return back tostep 1105, using data in forms updated by the user. Also, after step1105, a decision point is reached on whether the re-forecasting ofpassenger and yield is completed (step 1106). If the re-forecasting isnot completed, the re-forecasting updates continue (step 1108, and thenproceed to step 1107). If the re-forecasting is completed, the revenuedata is updated with the reforecasted data (step 1109), proceeding thenback to the step 1101.

[0360] Below is a sample Yield Re-forecast model:

Forecast=YTD VAR*TGT YLD

[0361] Where

YTD VAR=(YTD YLD−YTD TGT YLD)/YTD TGT YLD*100

[0362] YTD YLD=Year to Date Actual Yield

[0363] YTD TGT YLD=Year to Date Target Yield

[0364] TGT YLD=Target Yield

[0365] Boundary Conditions: YTD VAR>LowerLimit & <Upper Limit

[0366] Boundary conditions are applied to the YTD yield variations.These boundary conditions are set system parameters in the RPS 100.These values can be changed at any time and the Yield Re-forecastingprocess 504 can be re-run. In the preferred embodiment, an Upper limitis set at +5% and a Lower limit is set at −10%.

[0367] 8.2.1 Re-Forecasting Yield Simulation

[0368] As shown in the Yield Re-forecasting Simulation example below:Simulation Parameters SS Date: 21 June POS: UAE (DUBAI) OD: LHRDXB Comp:Y Travel Month: Jul 02 JULY JULY YTD YLD YTD TGT YLD TGT YLD ACTUAL YLD1,313 1,244 1,314 1,336 YTD VAR = (YTD YLD − YTD TGT YLD/(YTD TGT YLD) *100 = (1,313 − 1,244)/1,244 * 100 = 5.5%

[0369] As it exceeds the boundary condition of upper limit of 5%, theYTD VAR is capped to 5%. $\begin{matrix}{{{Forecast}\quad {Yield}}{\quad } = {{YTD}\quad {VAR}*{TGT}\quad {YLD}}} \\{{= {1.05*1,314}}} \\{{= {1,379}}} \\{{Actual}{\quad } = {1,336}} \\{{{Forecast}\quad \% \quad {Var}} = {{\left( {{1,379} - {1,336}} \right)/1},336}} \\{{{Forecast}\quad {Error}}{\quad } = {3\%}}\end{matrix}$

[0370] 8.2.2 Special Cases Handling

[0371] Case I: When Traffic/Fare mix changes, e.g., for Germany, YTD YLDvariance is not capped in these cases. Actual YTD yield variance is usedfor yield Re-forecast.

[0372] In the Re-forecasting Yield Simulation—Currency Strengtheningexample below: Simulation Parameters Currency: EUR SS Date: 21 June POS:Germany OD: DUSDXB Comp: Y Travel Month: Jul 02 JULY JULY YTD YLD YTDTGT YLD TGT YLD ACTUAL YLD 186 145 162 200 YTD VAR = (YTD YLD − YTD TGTYLD)/(YTD TGT YLD) * 100 = (186 − 145)/145 * 100 = 28%

[0373] The YTD VAR exceeds the boundary condition of upper limit of 5%,but is not capped to 5%, since this represents the special case-handlingscenario. Therefore, the value of 28% is retained. $\begin{matrix}{{{Forecast}\quad {Yield}}{\quad } = {{YTD}\quad {VAR}*{TGT}\quad {YLD}}} \\{{= {1.28*162}}} \\{{= 207}} \\{{Actual}{\quad } = 200} \\{{{Forecast}\quad \% \quad {Var}} = {\left( {207 - 200} \right)/200}} \\{{{Forecast}\quad {Error}}{\quad } = {3.5\%}}\end{matrix}$

[0374] Case II: New Routes. New O&Ds for these O&Ds, re-forecasted yieldwill be target yield for the current budget year.

[0375] Case III: For routes where extra frequency is implemented, yieldshould be reviewed for any abnormality.

[0376] Exceptional cases: For the months with zero yield forRe-forecasted months, average yield of the O&D can be used and populatedduring re-forecasting:

Average Yield=Sum of Actual Revenue for flown months/Sum of PAX flown.

[0377] Compartment: Y Yield PAX Revenue POS O&D April May June April MayJune April May June POS O&D 10 12 10 10 100 120 1 1

[0378] Therefore, the Average Yield for the month of April=11

8.2.3 Exception Reports for the Re-Forecasted Values

[0379] 8.2.3.1 Introduction

[0380] The purpose of the exception reports (see 1107 in FIG. 11A) is tolist the POSs and O&D combinations for the PAX and Yield Re-forecastingmonths, that have qualified for the exception criteria. This user wouldthen use the update form to correct the Re-forecasted values for theserecords. These exception reports can be generated by the RPS 100 tobring out exception records for PAX or yield. An example exceptionreport is shown in FIG. 11B, which shows revenue, PAX and yield for theregions of Europe, GCC (Gulf Cooperation Council), MEA (Middle East) andWAPR (West Asia/Pacific Rim), a revenue performance graph, and are-forecasted revenue graph. Network revenue, PAX and yield are alsoshown. FIG. 11C is a screen shot obtained by clicking on the “monthlevel” link in FIG. 1B.

[0381] Exception: The From and To range of numbers can be the same. Inthis case the report will fetch records (POS and O&Ds) which are havinga variance % between the Re-forecasted value and the baseline valueequal or above the numeric value. By entering a different To rangenumber (which has to be larger than the From range number), the reportwill fetch records (POS and O&Ds) that have a variance between theRe-forecasted value and the baseline value that fall in the From and Torange specified.

[0382] Option: PAX Re-forecasting 503 will apply the exception criteriaof variance % against the baseline\Re-forecasted PAX value and Yieldwill apply it against the baseline\Re-forecasted Yield values. Theappropriate reports will also get generated. The Yield option can selectthe currency for the O&D Yield values on the report.

[0383] POS Summary/Detailed: The summary option lists all the POSs thatare having O&Ds whose Re-forecasted values are qualifying for theexception criteria. The Detailed option displays both the POSs and theO&Ds whose Re-forecasted values are qualifying for the exceptioncriteria. Both reports can optionally display the Re-forecasted month ina cross tab fashion.

8.2.3.2 POS Summary (PAX)

[0384]FIGS. 12-15 show examples of Exception reports. FIG. 12illustrates an example of a POS Summary generated by the RPS 100, asrelated to the exception report discussed above. As shown in FIG. 12,three POS's are shown, Australia, India (Northern) and India (Southern).Four months are shown—September 2002, October 2002, November 2002 andFebruary 2002 are shown. In FIG. 12, “440” in the right hand column isthe yield. “15” is the numeric value entered by the user in the‘Exception’ selection.

[0385] Number of O&Ds—is the number of O&Ds for the POS which hasqualified for the variance % criteria.

[0386] Similarly, PAX is the sum of the PAX values of all the O&D's thatwere selected.

[0387] All parameter information will appears in the report header asshown.

[0388] POS Detailed (PAX) report is shown in FIG. 13. The primarydifference between the report of FIG. 12 and the report of FIG. 13 isthe breakdown by a particular POS.

8.2.3.3 POS Summary (Yield)

[0389]FIG. 14 is another example of a report related to yield forgeneral of POS's, and FIG. 15A is an example of a POS detailed reportfor a region. All parameter information will appear in the report headeras shown.

[0390]FIGS. 15B-15U are samples of Region Summary and Network summaryfrom Re-forecast Region Summary in spreadsheet form that can be producedby the RPS 100. FIGS. 15B-15E show Re-forecast summary for the entirenetwork for compartments TL (total), F, J and Y, respectively, FIGS.15F-15I show Re-forecast summary for the ENA (Europe-North America) forcompartments TL, F, J and Y, respectively, FIGS. 15J-15M showRe-forecast summary for the Gulf Cooperation Council (GCC) countries forcompartments TL, F, J and Y, respectively, FIGS. 15N-15Q showRe-forecast summary for the Middle East (MEA) for compartments TL, F, Jand Y, respectively, and FIGS. 15R-15U show Re-forecast summary for theWAPR (West Asia/Pacific Rim) for compartments TL, F, J and Y,respectively.

8.2.3.4 Re-Forecasted Data Update Facility

[0391] This update facility comprises two forms:

[0392] a. Query form

[0393] b. Update form

[0394] The Query form fetches the requested record for update onto theUpdate form. The Query form has the following selection criteria: Regionlist of values of all Regions POS list of values of the POSs of theRegion selected. Compartment F/J/Y

[0395] The Update form retrieves the baseline and the Re-forecasted datafor all the O&Ds from the POS and Comp selected. Both Re-forecasted PAXand Re-forecasted Yield values can be updated and saved.

[0396] An audit trail for all updates taking place via this update formcan also be performed.

8.2.3.5 Re-Forecasted Data Reports

[0397] The purpose of the Re-forecasted Data reports, which the RPS 100can generate, is to list the details of the Re-forecasted data 502generated by the Re-forecasting process 102. Both reports can displaythe Re-forecasted month in cross-tab fashion.

[0398] 8.2.4 Re-Forecasting Model Inference

[0399] Results have shown minimum forecast error, and that the selectedmodel is well suited to yield Re-forecasting.

[0400] 8.3 Re-Forecast PAX Constraining Logic

[0401] Once the PAX Re-forecasting 503 is done, the PAX forecasts areconstrained to adjusted capacity available in each route. Adjustedcapacity is the capacity multiplied by a predetermined factor selectedby the system administrator.

[0402] For a particular location (e.g., LHR), capacity can bemathematically represented as${\sum\limits_{i = 1}^{i = m}{\sum\limits_{j = 1}^{j = n}{P_{i}\left( {{LHR} - {Destination}_{j}} \right)}}}<={{adjusted}\quad {capacity}\quad {of}\quad {LHR}\text{-}{DXB}}$

[0403] where i=number of POS which has LHR as origin, and

[0404] j=number of destinations originating from LHR for a POS.

[0405] For example, if LHRDXB (London-Dubai) Economy capacityutilization is assumed as 95% in the month of December 2002,$\begin{matrix}{{{Actual}\quad {Economy}\quad {Capacity}\quad {of}\quad {LHRDXB}} = {25,513}} \\{{{Adjusted}\quad {Capacity}}{\quad } = {0.95*25,513}} \\{{= {24,237}}}\end{matrix}$

[0406] In carrying out the Re-forecasting of Economy class PAX for themonth of December 2002, it is desirable to ensure that forecast PAX forall POS originating from LHR to various destinations should not crossthe adjusted capacity i.e. 24,237. Hence, once the PAX Re-forecast 802is done, it is constrained as per the flow chart of FIG. 16. Hence, inany Leg, Re-forecast PAX will not be higher than the adjusted capacity.Adjusted capacity of all Legs is calculated based on its anticipatedutilization rates.

[0407] The constraining process starts at step 1601. At step 1602, anunconstrained reforecasted PAX demand of POS-O&D-Comp-Travel Monthcombination is accessed. At step 1603, the POS-O&D and each POS segmentis split. At step 1604, this particular segment is selected. At step1605, all the forecast all the segments for all the POS are summed. Atstep 1606, a decision point is reached as to whether all the POSs arecovered for the selected segment if no, then at step 1607, another POSis selected that has the same segment forecast. If yes, at step 1608,another segment is selected. If not all segments are covered (step 1609)the process goes back to the segment selection step 1604. If allsegments are covered, the process goes to step 1610, which includes thesplitting of the aggregated segment forecast according to the segmentroute breakup ratio.

[0408] At step 1611, the segment route breakup is mapped into segmentand leg and route combinations. At step 1612, the leg and routeforecasts are aggregated. At step 1613, the leg and route forecasts arematched to leg and route capacity. At step 1614, if the leg and routeforecast is less than a certain percentage of leg capacity, a reductionfactor is appraised to match the planned capacity (step 1615) if theforecast is less than the capacity, the process continues for other legs(step 1616). At 1617, reverse formulation of leg and route to legsegment route is carried out. At 1616, reverse formulation of legsegment route to segment route is carried out. At step 1619, reverseformulation of segment route to flown segments is carried out. At step1620, origin and destinations are built up with flown segments. At step1621, constrained POS-O+D forecast is created. The process ends at step1622.

[0409] 8.3.1.1 Update of Re-Forecasted PAX/Yield Data into the RevenueData

[0410] This is a process that is triggered by the user after theRe-forecasted data has been reviewed. It updates the revenue databaseline for the Re-forecast month from the Re-forecast data store. Therevenue is always recomputed using the PAX and the yield values afterthey have been updated by the Re-forecasting.

[0411] A system parameter “Re-forecasting Completed” indicates whetheror not the PAX and Yield Re-forecasting process 102 has been completed.The update revenue data takes place only if this flag is set to‘Completed’ status.

[0412] The Re-forecasting process 102 may record details in the log. Thedetails may include the following:

[0413] 1. Start date & time of process;

[0414] 2. User id;

[0415] 3. POS-wise Re-forecasted PAX totals (updated to revenue data).

[0416] 8.4 Re-Forecast Effectiveness Measurement

[0417] Effectiveness of Re-forecasting process 102 (PAX, Yield) isevaluated based on the last year Re-forecast data and actual data. Datafor December 2001, January 2002, February 2002, March 2002 are evaluatedfor Total, F, J, Y compartments (i.e., First, Business, and Economyclass compartments) and comparative results are given in FIGS. 17-24. Inall cases, it has been shown empirically that the Re-forecast models arehighly effective. For example, as may be seen in FIG. 17, the variancenumbers are 6% or less. In FIG. 20, the variance numbers are under 3%,which is quite good.

[0418] As an example, December 2001-March 2002 Re-forecast performance(Comp: Economy), of one POS is shown in FIG. 25. This figureillustrates, in percentage terms, the effectiveness of the forecastedvalue (in percent, compared to actual value), for each parameter (PAX,yield and revenue), by month (horizontal “axis”) and by POS (verticalaxis).

[0419] 9.0 Demand Estimation 103

[0420] 9.1 Introduction

[0421] Demand Estimation 103 is the process of forecasting the PAXdemand that should materialize in the target year. Once validated andapproved after the optimization process, it forms the PAX targets to beachieved for the target year. Note that estimation is also done for theexpected yield.

[0422] Demand Estimation 103 is preferably carried out at the POS andO&D and Compartment level for every month in the target year. A DemandEstimation module within the RPS 100 generates the PAX demand data forthe target year for all the months of the target year at the POS and O&Dand Compartment level. Inputs to the module are as follows:

[0423] Monthly Flown PAX data at the O&D, POS, Region level for thethree compartments for the past ‘N’ years (for example, N=5);

[0424] Monthly MIDT 202 bookings data at the O&D, POS levels for thethree compartments for the past ‘M’ years (for example, M=5, or M=N,although that need not always be the case); and

[0425] The O&D Capacity data for the current year and the target year.

[0426] 9.2 Demand Estimation Functional Process

[0427] Demand Estimation 103 includes the following processes:

[0428] Derivation of Actual Traffic Growth factor;

[0429] Derivation of Market Share Growth factor from MIDT 202 data;

[0430] Derivation of O&D Capacity Growth Factor;

[0431] A facility to manually edit and store Effective Growth Factors;

[0432] A process to trigger the unconstraining of the baseline demandbased on the factors derived;

[0433] Reports for displaying the final demand ensuing after theunconstraining process;

[0434] Exception reports for displaying the factors derived;

[0435] An audit trail for manual alterations on the factors andexecution of unconstraining process; and

[0436] PAX Demand Estimation for routes with less than one year flowndata.

[0437] 9.3 Derivation of Actual Traffic Growth Factor

[0438] The RPS 100 includes a process to extract and store the ActualTraffic Growth Factor (sometimes referred to as “Actual Growth Factor”)from the commercial database. This process should extract the growthrate (in %) year over year of the flown PAX from the commercialdatabase. This growth rate (in %) is called the Growth Factor (GF).

[0439] The growth rate is used at the Compartment and Year and Month andPOS and O&D level. The RPS parameter “Demand Estimation No. of PreviousYears” initializes the year from which the growth factor needs to beextracted.

[0440] The GF is calculated at the O&D level only for those O&Ds thataccount for the top 80% revenue generation of the POS. The O&Ds, whichaccount for the remaining 20%, the Growth Factor should be calculated atO&D level with the Growth Factor pertaining to POS. The RPS 100calculates the previous years GF based on the system parameter of theCurrent Year, and the number of previous years:

[0441] The Number of Previous Years for the Actual Traffic GrowthFactor=5

[0442] The Year over Year Growth Factor for a month is computed asfollows:

(This Year−Last Year/Last Year)*100

[0443] Actual Traffic Growth Factor=Weighted Average Growth

[0444] Factor for previous years defined as a system parameter.

Actual Traffic Growth Factor=(W1*G1+W2*G2+ . . . Wn*Gn)/(W1+W2+ . . .+Wn)

[0445] where W1 to Wn are weights for n years, and G1 to Gn are growthfactors for n years.

[0446] The Actual Traffic Growth Factor computation is preferablymodularized.

[0447] 9.4 Derivation of Bookings Growth Factor from MIDT Data

[0448] The RPS 100 includes a process to extract the Market Share GrowthFactor, to formulate the market growth factors, and to store them.

[0449] The MIDT 202 Database is interrogated to extract the Market Share(in %) at the Compartment and Year and Month and POS and O&D level.

[0450] The O&Ds for which the Actual Traffic Growth Factor has beencomputed are used to retrieve O&Ds from MIDT 202. The MIDT Market GrowthFactor is computed only for the O&Ds that are retrieved.

[0451] MIDT 202 Market Share for the Number of Previous years that areinitialized from the target for which the growth factor needs to beextracted are parameterized, for example:

[0452] Current Year=2002

[0453] Number of Previous years of the MIDT Market Share=5

[0454] Thus, for 2002, the MIDT Growth Factor should be calculatedstarting from 1997 through 2001:

[0455] Market Share=Monthly Bookings/Monthly Total Bookings.

[0456] The Market Share Growth Factor should reflect the year over yearvariation and the computation is as follows:

(This Year−Last Year/Last Year)*100

[0457] Weighted Market Share Factor=(W1*M1+W2*M2+ . . . Wn*Mn)/(W1+W2+ .. . +Wn)

[0458] Where W1 to Wn are weights for n years, and M1 to Mn are MarketShare for n years.

[0459] After deriving the Growth Factor, it should be compared with thefollowing formula and use the appropriate MIDT Growth Factor to derivethe Effective Growth Factor: If Market Share GF < = 15%, MIDT GrowthFactor = 4% Market Share GF is between 16% to 30%, MIDT Growth Factor =6% Market Share GF > 30%, MIDT Growth Factor = 10% Market Share GF isnegative, MIDT Growth Factor = 10% From the above example, the MIDTGrowth Factor should be = 4%, which should be used to derive theEffective Growth Factor.

[0460] 9.5 Derivation of O&D Capacity Growth Factor

[0461] The growth rate of the O&D capacity between the target year andthe current year is extracted. These O&D capacity values for both theyears are available from the RPS database 207.

[0462] The RPS database 207 is interrogated for the O&D Capacity of thetarget year and the current year. The O&D Capacity is extracted at theCompartment and O&D level for all the months of the target year.

[0463] The monthly growth rate and the total yearly growth rate iscalculated and stored for each O&D.

[0464] The monthly year over year Capacity Growth Factor is computed asfollows: (Target Month (Target Year)−Target Month (Current Year)/TargetMonth (Current Year))*100

[0465] 9.6 Derivation of Effective Growth Factor

[0466] The RPS 100 includes a process to compute the Effective GrowthFactor.

[0467] Then Target Traffic Growth Factor=Actual Traffic GrowthFactor+MIDT 202 Growth Factor

[0468] If the Target Traffic Growth Factor=>the Capacity Growth Factor,the Target Traffic Growth Factor is applied to the Effective GrowthFactor.

[0469] Thus, Effective Growth Factor=Target Traffic Growth Factor

[0470] Example: POS: UK (Southern) O&D: DXBLGW Actual Traffic MIDTGrowth Target Traffic Capacity Effective Growth Factor Factor in GrowthFactor Growth in Growth Factor Month in Percentage Percentage inPercentage Percentage in Percentage April 03 10 4 14 0 14

[0471] Target Traffic Growth Factor<Capacity Growth Factor:

[0472] If the Target Traffic Growth Factor<the Capacity Growth Factor,apply an average of Target Traffic Growth Factor and Capacity GrowthFactor to the Effective Growth Factor.

Effective Growth Factor=(Target Traffic Growth Factor+Capacity GrowthFactor)/2.

Effective Growth Factor=(14+100)/2=114/2=57

[0473] Example: POS: UK (Southern) O&D: DXBLGW Actual Traffic MIDTGrowth Target Traffic Capacity Effective Growth Factor Factor in GrowthFactor Growth in Growth Factor Month in Percentage Percentage inPercentage Percentage in Percentage April 03 10 4 14 100 57

[0474] Example: ‘Y’—Comp ‘LHRMEL’ O&D the Capacity Growth Factor derivedfor April 2003 is 50.

[0475] This is applied to POS where appropriate, to compute theEffective Growth Factor: Capacity Growth POS Compartment O&D Factor UKSouthern Y LHRMEL 50 UK Northern Y LHRMEL 50 UK Central Y LHRMEL 50

[0476] 9.7 Facility to Manually Edit and Store Effective Growth Factors

[0477] The RPS 100 optionally includes a Query/Update Form combination,by which the Growth Factor from all the three processes (Derivation ofActual Traffic Growth Factor, Derivation of Booking Growth factor fromMIDT 202 data, and Derivation of O&D Capacity Growth Factor) can bedisplayed and appropriate changes can be made to the Effective GrowthFactor values.

[0478] This Query Form facilitates accepting the parameters to generatethe query for the Effective Growth Factor, to be displayed in an UpdateForm.

[0479] The user can edit the Effective Growth Factor. The user can querythe Effective Growth Factor based on Region, POS, and Month:

[0480] Actual Traffic Growth: This displays the growth for the ‘N’ yearsdefined in the parameters, the data at the O&D, POS and Region levelsfor the F, J & Y Compartments. Currently ‘N’=5.

[0481] MIDT Market Share: This displays the share for ‘M’ years definedin the parameters, the data at the O&D, POS and Region levels for the F,J & Y Compartments. Currently ‘M’=5.

[0482] On completion of the edit of the Effective Growth Factor, the RPS100 prompts to save on exit or to cancel (not to accept changes). Thesaved changes can be captured in the audit trail.

[0483] 9.8 Process to Trigger the Unconstraining of the Baseline PAXDemand

[0484] This is a process that uses the Effective Growth Factor valuesfor each Comp and POS and O&Ds, and inflates/deflates the baselinevalues to arrive at the PAX demand values for each month based on thefactors derived. The trigger process computes the PAX demand based onthe Effective Growth Factor at Comp and O&Ds and POS level for eachmonth in the target year. The Revenue data should be updated at the sametime. On completion of the unconstraining process, the user should beable to generate reports and view the PAX demand based on factorsderived.

[0485] 9.9 Demand Estimation for Routes with Less Than One Year FlownData

[0486] The RPS 100 includes a process to compute PAX demand for themonths in the current year, where the O&D were not operational, or flowndata is not available.

[0487] This process computes the average PAX demand from the data offlown months available at POS-O&D-Compartment level and populates PAXdata for the months where flown data is not available in thecorresponding POS-O&D-Compartment level. The computation is as follows:Average PAX=Sum of Actual PAX for flown months/Number of flown months.

[0488] In the example below, the Actual PAX is available for the monthof May and June and the PAX demand should be populated for the month ofApril. PAX demand POS O&D April May June POS 1 O&D1 15 10 20

[0489] Sum of Actual PAX for flown months=10+20=30.

[0490] Number of flown months=2 (May and June).

[0491] Average PAX=Sum of Actual PAX for flown months/Number of flownmonths.

[0492] Average PAX=30/2=15.

[0493] 9.10 Demand Estimation Derivation

[0494] Demand Estimation 103 is carried out for PAX and yield. DemandEstimation 103 considers internal growth (i.e., the airline's owntraffic growth), as well as market growth (i.e., traffic on all theairlines for a particular O&D). Due weight is given to recent pastgrowth in the market as well as the airline's own growth in estimatingfuture trends. In some circumstances, internal growth may be used toderive market growth, and vice versa (in other words, market growthserves as a proxy for internal growth, or vice versa).

[0495] Demand Estimation 102 follows the Re-forecasting process 102.Once the Re-forecasting 102 is done for the PAX and yield for theremaining months of the current financial year (e.g., 2002-2003), actualdata from April 2002-August 2002 and Re-forecast data from September2002-March 2003 forms or cornerstone for the demand estimations for thenext budget year (e.g., 2003-2004). An optional feature allows theindividual POSs to “negotiate” with sales and yield management online,in real time, and to “escalate” the issue using E-dialogue if they areunable to reach an agreement on the expected targets for the next budgetyear. This aids in the transparency of setting targets, allowing greater“buy in” into the targets by the sales force.

[0496]FIGS. 26A-26G illustrate the process of using E-dialogue toachieve buy-in from the various constituencies within an airline intothe targets. FIGS. 26A-26B show an initiation (or retrieval) of anE-dialogue (and should be viewed as a single figure). As shown in FIGS.26C-26D, a “partially agreed item” exists (these two figures are part ofthe same screen and should be viewed as a single figure). As shown inFIG. 26E-26F, a “disagreed item” exists (these two figures are also partof the same screen and should be viewed as a single figure). As shown ina screen shot of FIGS. 26G-26G (which should be viewed as a singlefigure), a summary of the E-dialogue is displayed, showing the agreeditems, the disagreed items, and the open items. As noted above, by goingthrough this process, the target setting process can arrive at thetargets that are agreed to by the various constituencies within anairline.

[0497] 9.11 PAX Demand Estimation—Additional Factors

[0498]FIG. 27 shows an I-P-O diagram of PAX Demand Estimation 2701. Asshown in the I-P-O diagram of FIG. 27, the primary inputs 2702 to thePAX Demand Estimation Process 2701 are the last 5 years flown coupons(O&D-Comp-Travel month-wise), the last 5 years MIDT 202 data forO&D-Comp-Travel month combinations, and O&D Capacity for the currentyear and the target year.

[0499] The number of years considered for the actual flown data can beentered by the user. Hence, the RPS 100 considers the number of yearsfor the PAX Demand estimation 2701, and the MIDT 202 data for the samenumber of years, currently, set as 5 years. Therefore, the RPS 100considers last 5 years actual flown and MIDT 202 data for estimating thePAX demand for budget year (e.g., 2003-2004). The output 2703 of the PAXDemand Estimation Process 2701, as shown in FIG. 27 is the estimateddemand for passenger traffic for the budget year for various POS-Originand Destination-Compartment-Travel Month combinations.

[0500] 9.11.1 Effective Growth Factor Derivation

[0501] As shown in the flow chart of FIG. 28, for the derivation ofEffective Growth Factor (EGF), which is used for determining theexpected demand, actual passenger growth, market growth and capacitygrowth are considered. Hence, this model takes into account allinfluences (internal, as well as external) to accurately predict demandin the market.

[0502] Before calculating the EGF, Passenger Growth Factor and MarketGrowth Factor are calculated. Passenger Growth Factor (PGF) and CapacityGrowth Factor (CGF) are used in deriving the Effective Passenger GrowthFactor (EPGF). While calculating the passenger growth and market growthfactors, weighted average method is used to give the preferentialimportance to the recent growth instead of simple average. Aftercalculating the Weighted Passenger Growth Factor (WPGF), Weighted MarketGrowth Factor (WMGF) is determined. Depending on the Weighted MarketGrowth Factor, Target Market Share (TMS) is assigned. This reflects thepotential to capture the market depending on the market growth. TMS isassigned on increasing rate when market share grows, to have a biggerpresence in the market where the potential exists to sell it. The flowchart of FIG. 28 depicts the steps followed in deriving the EGF, whichis used in estimating the PAX demand.

[0503] As shown in FIG. 28, the process of estimating the EffectiveGrowth Factor (EGF) begins at the start step 2201. The user then inputsthe number of years of actual flown passenger data (step 2202). The userthen inputs the number of years of MIDT 202 data (step 2203). The usercan then input current year origin and destination capacity (step 2204).The user then inputs the budget year origin and destination capacity(step 2205).

[0504] The Weighted Passenger Growth Factor is then calculated (step2206). The Weighted Market Growth Factor is then calculated (step 2207).A decision point is then reached as to whether the Weighted Market ShareFactor is less than zero (step 2208). If it is, then the Total MarketShare (TMS) is taken as 10%. If it is not, the next decision point iswhether the Weighted Market Share Factor is less than 15% (step 2210).If it is, then the TMS is taken as 4% (step 2211). If it is not, thenext decision point is whether the weighted market share factor is lessthan 30% (step 2212). If it is, then the TMS is taken as 6% (step 2213).If it is not, the TMS is taken as 10% (step 2214).

[0505] After calculating the weighted market share factor, and thecalculation of the target market share (step 2215), combined trafficgrowth is calculated (step 2216). Origin and Destination Capacity GrowthFactor is then calculated (step 2217). If CTG>CGF (step 2218), theeffective demand factor (EDF) is taken as (CTG+CGF)/2 (step 2220). IfCTG is greater than CGF, then EGF=CTG (step 2219). The process can thenend, or optionally return to the start step 2201.

[0506] 9.11.2 Wighted Passenger Growth Factor (WPGF)

[0507] The Weighted Passenger Growth Factor (WPGF) is a parameter in thePAX demand estimation process 2701. Passenger growth for the last Xyears (currently set as X=5) is considered in calculating WPGF. Theweighted average is considered instead of simple average. Weights havebeen chosen such that recent trend should have higher influence inestimating demand for budget year. Accordingly, weights have beenselected in one example as shown below: Yr. No Year Weights 1 1998 0.052 1999 0.15 3 2000 0.20 4 2001 0.25 5 2002 0.35

[0508] A sample calculation is shown below:

[0509] POS: DXB, O&D: LHRDXB, Comp: Economy, Travel Month: July 2003Jul-97 Jul-98 Jul-99 Jul-00 Jul-01 Jul-02 Pax 1,161 1,025 1,339 9661,103 1,227 Growth Factor −12%  31%  −28%  14%  11%  Weights 0.05 0.150.20 0.25 0.35

9.11.3 Weighted Market Share Factor (WMSF)

[0510] While estimating the demand for the budget year, the last 5years' market growth can also been considered. Weights used forpassenger growth may be used in this case also. These weights can bechanged by the user. As with the WPGF, recent years' market growth getpredominance compared to other past years.

[0511] A sample calculation is shown below:

[0512] POS: DSB, O&D: LHRDXB, Comp: Economy, Travel Month: July 2003Jul-97 Jul-98 Jul-99 Jul-00 Jul-01 Jul-02 EK-Pax 1,205 991 1,351 1,304Total Pax 3,407 3,366 3,591 3,267 Market Share % 35%  29%  38%  40% Weights 0.15 0.20 0.25 0.35

[0513] WMSF=(0.35*0.4+0.25*0.38+0.20*0.29+0.15*0.35)=34%

[0514] 9.11.4 Target Market Share (TMS)

[0515] Target Market Share (TMS) is the market share that the airlinefocuses on. Once the WMGF is determined, a certain Target Market Shareis assigned to POS-O&D-Comp combinations by taking into considerationmarket potential. TMS is applied on monthly basis. The Target MarketShare value depends on WMSF as given in the TMS matrix discussed below.

[0516]FIGS. 29A-29D are screen shots that illustrate the details ofcalculating the PAX demand—including WPGF, TMS, and capacity details, intabular form, as discussed above. FIG. 29E is an illustration ofcapacity highlights, including breakdown by compartment, for aparticular region (Europe), and new routes. This figure may be used, forexample, to assist a user during E-dialogue (discussed above, see alsoFIGS. 26A-26G), particularly when setting targets for a new route.

[0517] 9.11.5 TMS Matrix

[0518] The table below gives the value of Target Market Share that maybe assigned for each POS-O&D-Comp-Travel Month combination, when theWMGF attains the value specified in the header. Less Between BetweenAbove than 0% 1% and 15% 16% and 30% 30% 10% 4% 6% 10%

[0519] As calculated in the above example, WMGF is 34% and it falls inthe last band, which is >30% category. Thus, Target Market Share forthis market growth is 10%.

[0520] 9.11.6 Combined Traffic Growth (CTG)

[0521] Once the WPGF and TMS are calculated, Combined Traffic Growth iscalculated as shown below for each POS-O&D-Comp-Travel Month:

[0522] Combined Traffic Growth=Weighted Passenger Growth Factor+TargetMarket Share. A sample calculation is as follows:

[0523] POS: DXB, O&D: LHRDXB, Comp:Economy, Travel Month: July 2003$\begin{matrix}{{{Combined}\quad {Traffic}\quad {Growth}} = {6 + 10}} \\{= {16\%}}\end{matrix}$

[0524] 9.11.7 Capacity Growth Factor (CGF)

[0525] For calculating the Capacity Growth Factor (CGF), capacities ofcurrent financial year and budget year are considered forO&D-Compartment-Travel Month combinations.

[0526] A sample calculation for O&D: LHRDXB, Comp: Y, Travel Month: July2003 is shown below. July - 02 July -03 CGF Capacity 24,021 25,513 6%

[0527] 9.11.8 Effective Growth Factor (EGF) Example

[0528] As discussed above, in order to derive the EDF, Combined TrafficGrowth (CTG) is compared with Capacity Growth Factor (CGF) and relevantformula is used: IF CTG > CGF Then Effective Demand Factor = CTG EGF =(CTG + CGF)/2

[0529] In the example discussed above, the first condition holds true,i.e., CTF>CGF, therefore:

[0530] EGF=CTG

9.11.9 PAX Demand Estimation—Sample Calculation

[0531] With the help of EGF, demand can be derived by multiplying EGF byCurrent Year actual data.

[0532] Sample Calculation:

[0533] POS: DXB, O&D: LHRDXB, Comp: Economy, Travel Month: July 2003

[0534] July 2002 Actual PAX=1,227

[0535] Demand for July 2003=Effective Demand Factor*July 2002 ActualPAX=1.16*1,227

[0536] 9.12 Effectiveness of Passenger Demand Estimation

[0537]FIG. 30 shows a Demand vs. Actual comparison for the LHRDXB routefor July 2002. The graph in FIG. 30 shows the comparison of Target andActual PAX for the top three Points of Sale in LHRDXB route. PAX targetof July 2002 is compared with Actual PAX July 2002 (both Economy class).It clearly shows that proposed estimation method matches the marketpotential.

[0538] 9.13 Yield Demand Estimation

[0539] 9.13.1 Introduction

[0540] Yield estimation is the process of forecasting the yield thatshould be used to compute the Target Revenue for the target year. Yieldestimation is carried out at the POS and O&D and Compartment level forevery month in the target year. The yield estimation module generatesthe demand yield data for all the months of the target year at the POSand O&D and Compartment level.

[0541] 9.13.2 Functional Requirements

[0542] The following are the functional requirements for the yielddemand estimation:

[0543] Derivation of Yield Growth Factor;

[0544] Reports for displaying the factors derived;

[0545] A process to trigger the unconstraining of the baseline PAXdemand based on the Yield Growth Factor derived;

[0546] Reports for displaying the final PAX demand after theunconstraining process; and

[0547] Demand Yield Estimation for routes with less than one year offlown data.

[0548] 9.13.3 Derivation of Yield Growth Factor

[0549]FIG. 31A shows an I-P-O diagram for Yield Demand Estimation. Asshown in FIG. 31A, the Yield Demand Estimation process 3101 uses asinput 3102 the last 5 years of yield data from CVIEW 201. The output3103 of the Yield Demand Estimation process 3101 is Estimated DemandYield for the budget year at POS Origin and Destination-Comp-Year-Monthlevel.

[0550] An example of a graph illustrating average fare (yield) growth isshown in FIG. 31B. As shown in FIG. 31B, average fares and average faregrowth for the years 1997-2002 (here, N=5 years) is shown in tabular andgraphical form. (The word “average” on the top left is cut off in thefigure).

[0551] Yield Demand Estimation 3101 is based on projecting the weightedtrends into the future. It takes into consideration last ‘N’ years, andyear-over-year monthly variance of actual yield for POS-O&D-Compcombinations. As an example, ‘N’ may be set as 5 years.

[0552] Sample weights used to calculate the weighted average areillustrated above. Once the year-over-year variances are determined,these variances are multiplied by the corresponding weights. Afterdetermining the weighted average of the yield variance, it is multipliedwith the current year yield actual to get the budget year demand yield.This is illustrated in the sample calculation in FIG. 32, discussed insection 9.14?.

[0553] The RPS 100 includes a process to extract and store the YieldGrowth Factor from the commercial database.

[0554] The growth rate is fetched at the Compartment and Year and Monthand POS and O&D level. The system parameter “Demand Estimation No. ofPrevious years” initializes the year from which the Yield Growth Factorneeds to be extracted.

[0555] The Yield Growth Factor is calculated at the O&D and POS level.The Year over Year Growth Factor for a month is computed as follows:(This Year−Last Year/Last Year)*100

[0556] Average Yield Growth Factor=Weighted average yield growth factorof previous years defined as a parameter.

[0557] Average yield growth factor=(W1*Y1+W2*Y2 . . . +WnYn)/(W1+W2+ . .. +Wn)

[0558] Where W1, W2 . . . Wn are the weights, and Y1, Y2 . . . Yn arethe Yearly yield growth factor.

[0559] The Average Yield Growth Factor computation is preferablymodularized, and the RPS 100 facilitates the change of the Growth Factorcomputation mechanism in the future.

[0560] The computed average yield growth factor for all the months iscompared to the Yield Capping Limits before it is applied. The Upper andLower limits for Yield Capping are parameterized as follows:

[0561] If the computed yield Growth Factor is below the Lower limit,then the Lower limit is applied.

[0562] If the computed Growth Factor is above the Upper limit, then theUpper limit is applied.

[0563] If the computed Growth Factor is between the Upper limit and theLower limit, then the computed Yield Growth Factor is applied.

[0564] If the Upper and Lower limits are not defined, then the computedYield Growth Factor is applied.

[0565] 9.13.4 Process to Trigger the Unconstraining of the BaselineDemand Yield

[0566] The RPS 100 includes a process that uses the final Demand Yieldfor each Comp and POS and O&Ds and inflates/deflates the baseline valuesto calculate at the Demand Yield values for each month based on thefactors derived.

Demand Yield=Actual Demand+(Actual Demand*Weighted average yield growthfactor)

[0567] The trigger process also computes the Target Revenue based on thefinal Yield Growth Factor at Comp and O&Ds and POS level for each monthin the target year:

Revenue Demand=PAX demand*Demand Yield.

[0568] The Unconstraining process should be performed on the RevenueData. On completion, the user can generate reports and view the RevenueDemand based on factors derived.

[0569] 9.13.5 Yield Estimation for Routes with Less Than One Year FlownData

[0570] The RPS 100 includes a process to compute the Demand Yield forthe months in the Current Year where the O&D were not operational orFlown Data is not available.

[0571] This process computes the average yield from the data of flownmonths, available at POS-O&D-Compartment level, and populates theaverage yield for the months flown data is not available, in thecorresponding POS-O&D Compartment.

EXAMPLE Compartment: Y

[0572] Yield PAX Revenue POS O&D April May June April May June April MayJune POS O&D 11 10 12 10 10 100 120 1 1

[0573] In the above example, the Actual Yield should be available forthe month of May and June and the Demand Yield should be populated forthe month of April=11. The computation is as follows:

[0574] Average Yield=Sum of Actual Revenue for flown months/Sum of PAXflown;

[0575] Sum of Actual Revenue=100+120=220;

[0576] Sum of PAX flown=20;

[0577] Average Yield=220/20=11;

[0578] Yield and revenue are preferably in POS local currency.

[0579] 9.13.6 Yield Demand Estimation—Sample Calculation

[0580] As shown in FIG. 32, for a particular POS-O&D combination, andthe July month of the years 1998-2002, yields, percent variance,year-to-year variance and weights are shown in the Table. The examplebelow illustrates how these numbers are used in a sample calculation.

[0581] Sample Parameters for Yield Demand Estimation 3101 are shown inFIG. 32. As shown in FIG. 32, $\begin{matrix}{\begin{matrix}{{Weighted}\quad {Average}\quad {of}} \\{{Yield}\quad {Variance}}\end{matrix} = {{0.05*\left( {- 11} \right)} + {0.15*\left( {- 17} \right)} + {0.2*\left( {- 13} \right)} +}} \\{\quad {{0.25*11} + {0.35*\left( {- 6} \right)}}} \\{\quad {= {{- 5.05}\%}}} \\{\begin{matrix}{{Projected}\quad {Yield}} \\{{for}\quad {July}\quad 03}\end{matrix} = {\text{(Weighted~~Variance)}*\text{(July~~02~~Actual~~yield)}}} \\{\quad {= {\left( {1 - 0.0505} \right)*184}}} \\{\quad {= 175}}\end{matrix}$

[0582] 9.14 Effectiveness of Yield Demand Estimation

[0583]FIG. 33 shows a yield demand estimation effectiveness graph. Byway of example, considering the trend of LHRDXB yield from the UnitedKingdom, it clearly shows that it has negative trend over the last 5years. Hence the projected yield for LHRDXB for United Kingdom for July2003 also matches the trend. A 5% drop in the yield in the nextfinancial year for UK-LHRDXB-Y-July 2002 combinations is expected. Theeffectiveness of yield estimation can be analyzed on the July 2002 data,i.e., comparison of Target yield for July 2002 (budgeted in the year2001), and actual yield that different POS achieved.

[0584] From the graph of FIG. 33, for UK, actual yield variation withregard to target yield is −3%, for UAE it is 4%, and for Canada 7%. Thisdemonstrates the reliability of the yield demand estimation model.

[0585] 9.15 Reports For PAX demand and Yield Estimation

[0586] 9.15.1 Exception Reports for Displaying the PAX Growth FactorsDerived

[0587] The RPS 100 includes a Parameter Form/Report combination that canbe used to list the factors derived by the processes of calculatingActual Traffic Growth Factor, MIDT 202 Market Share Growth Factor, andO&D Capacity Growth Factor.

[0588] The layout for the report is shown in FIG. 34. FIG. 34 displays aform for showing the Growth Factors for all the O&Ds for the POSs byRegion and the month selected. Instead of displaying the Growth Factorsfor O&Ds, the Growth Factors may be displayed by the POS and/or byregion and the month selected. The report form in FIG. 34 shows actualGrowth Factor for the given number of years, here, 5 years, or1998-2002); Actual Traffic Growth by Origin and Destination and byCompartment; Market Share Growth Factor for the five years, MIDT GrowthFactor, Target Traffic Growth Factor, Capacity Growth and EffectiveGrowth Factor.

[0589] 9.15.2 Exception Reports for Displaying the Yield Growth FactorsDerived

[0590] The RPS 100 includes a Parameter Form/Report combination, whichmay be used to list the derived Yield Growth Factors, and facilitatesacceptance of the parameters.

[0591] The sample reports, shown in FIG. 35, displays the month in crosstab fashion. As shown in FIG. 35, Actual Yield Growth may be displayedfor a particular origin and destination, combination by compartment, forthe five years at issue. Average Yield Growth is also displayed. Thesame report may be generated by POS, as well as by O&D level. Actualyield growth in FIG. 35 displays the growth for the ‘N’ years defined inthe parameters, the data at the O&D, POS and Regional levels for the F,J & Y Compartments.

[0592] 9.15.3 The Final PAX Demand and Yield After the UnconstrainingProcess

[0593] The RPS 100 includes a Parameter Form/Report to facilitate theuser running reports for the final demand ensuing after theunconstraining process. The Parameter Form facilitates acceptance of theparameters to generate the exception report for the Yield Growth Factorsderived.

[0594] The report (see layout in FIG. 36A) display the final demand forthe months (optionally in cross tab fashion). The revenue and yield forthe POS should be displayed in the respective POS Local Currency. Asimilar report may be generated for the POSs by Region and the months incross tab fashion, and which may include actual graphic, PAX demand, EGFfor selected months, and may show PAX, revenue, and yield informationfor the selected POSs.

[0595] The O&Ds should preferably be displayed in the descending orderof the Revenue Variance between the Actual and Demand in the DetailedReport. The POS should preferably be displayed in the descending orderof the Revenue Variance between the Actual and Demand in the SummaryReport.

[0596] 9.16 Summary of Demand Estimation

[0597] The demand forecasting process is summarized with respect to FIG.36B. As shown in FIG. 36B, to calculate the effective growth factor(3611), capacity data at POS level for N previous years (3601), capacitydata at a time period level for any previous years (3602), capacity dataat NOD level for N previous years (3603) and capacity data for a periodextending beyond 12 months (3604) are used. Also, market data 3608 isused to calculate market growth factor 3610, with the help of waitingfactors that are applied to the market data (3609).

[0598] Flown data at a POS level for M previous years (3605), flown dataat a time period level for M previous year (3606) and flown data at O&Dlevel for M previous years (3607) are used. Waiting factors are appliedto flown data (3612), and are also used to calculate the effectivegrowth factor (3611). Data from a commercial data base (3614) is used tocalculate actual growth factor (3613), which is also used as an inputand the calculation of the effective growth factor (3611). After the EGFis calculated, PAX demand forecast is calculated for the budget year(3615). Average fares and revenue for the budget year are estimated(3616) Optimization

[0599] As it is difficult to determine which traffic mix will be mostbeneficial for the airline under given conditions of capacity, demand,and expected average fare, Linear Optimization (LO) is used to optimizenetworth revenue. LO techniques deal with this type of problem bydetermining the optimal solution within given constraints.

[0600] Inputs to this process are market potential, average yield, andscheduled capacity. With the assistance of linear programmingtechniques, the RPS 100 produces the optimal traffic mix that isexpected to generate the maximum revenue for the airline. The marketpotential is determined on a POS-O&D-Compartment-Travel Month basis,yield is based on a POS-O&D-Compartment-Travel Month basis, and capacityis based on. Leg-Compartment-Travel Month basis.

[0601] Once the estimate for PAX and average fare is derived for eachPOS-O&D-Compartment-Travel Month combinations, it should be determinedwhich POS-O&D demand should be accepted, and which should be rejected,under limited capacity conditions. The decision to accept or reject ademand at this stage is a significant step in the Revenue PlanningProcess. A deterministic model of Linear Programming (LP) can be used.

[0602] The Linear Programming Optimization model determines the besttraffic mix (or “demand mix”) to maximize the revenue. In this case, theconstraints involve assets (i.e., aircraft), flying a leg from point Ato point B. Different POSs may be selling tickets for the same leg, atdifferent prices. For example, consider a flight from New York toLondon, and another flight from London to Stockholm. One POS (e.g.,Greece), may be selling a ticket for the New York—London leg at $700.Another POS (e.g., Frankfurt) may be selling a ticket for the NewYork—London leg at $500. However, the ticket sold by Frankfurt may befor a passenger who then goes on to Stockholm, for an additional $300.In other words, both Frankfurt and Greece are selling tickets where thepassenger demand “shares” a leg of the network. As another alternative,the ticket sold by Frankfurt may also include a return trip for anadditional $500, while the ticket sold by Greece is a one-way ticket.

[0603] In this example, the capacity constraints may be physicalconstraints (i.e., how many seats in each compartment on each aircraft),as well as legal constraints (i.e., international agreements limitingthe number of passengers an airline may carry per flight). The passenger(demand) constraints are the maximum number of passengers available tofly on each leg (or route, or sector) for each POS. The LinearProgramming Optimization model obviously cannot allow demand that isgreater than the capacity. A third constraint is fares.

[0604] As is clear from the above simplified example, a practicalairline network often contains hundreds (or thousands) of suchpossibilities. In other words, to maximize revenue for the wholenetwork, it is not enough to merely maximize revenue for oneleg-maximizing revenue for the New York—London leg does not necessarilymaximize the revenue for the “hub-and-spoke network” that consists ofNew York—London—Stockholm flights. Only by maximizing revenue on anetwork level (i.e., solving the network Linear Programming Optimizationproblem) is overall revenue actually maximized.

[0605] The Linear Programming Optimization model therefore determineshow to maximize network revenue given the capacity, demand, and fareconstraints. In the case of passengers competing for the same seat(where the passengers are willing to pay different fares), the modelensures that the overall revenue for the network (rather than at theroute/leg level) is maximized.

[0606] A budget plan (or “passenger budget plan”) defines how manypassengers an airline wants to have for the next year (or next timeperiod), and is derived from the Linear Programming Optimization model.In other words, the Linear Programming Optimization model helps setdemand targets for the next year (or next budget time period). TheLinear Programming Optimization model will therefore allocate Xpassengers to Greece at $700, and Y passengers to Frankfurt at $500. TheLinear Programming Optimization model ensures that the network revenueis maximized with the choices of X and Y.

[0607] Additionally, if it is known that for a given set of fares from agiven set of POSs, the resulting load factor is less than 100%, it ispossible to try a different set of fares (for example, 5% lower fares onsome routes) so as to determine whether overall network revenue ismaximized with a different set of fares. The targets may be set for thefinancial year after the current financial year, or for the next severalmonths, or the next month, etc.

[0608] In one embodiment, capacity is aggregated on a monthly basis,although other bases are possible (e.g., weekly, daily, etc.).

[0609] A leg is a single flight from point A to point B. A sector hasseveral legs (but only one-way). A route is a round trip (either one leg“there” and one leg “back”, or one sector “there” and one sector“back”). Multiple routes and sectors can traverse a single leg. Forexample, in the case of Boston—New York—London—Stockholm, the NewYork—London leg can be traversed by the Boston—New York—London—Stockholmroute, the New York—London—Stockholm route, the Boston—New York—Londonroute, etc.

[0610] Thus, the present invention provides a system and method ofsetting sales targets for an airline that includes estimating PAX demandand demand fares, performing linear optimization on a network level tomaximize overall network revenue based on the PAX demand and the demandfares and capacity constraints, and generating PAX target and targetfares for each POS for each O&D, compartment and month based on themaximized network revenue. Target fares may be calculated based on faretype, such that the fare type includes any one of one-way fares, returnfares, excursion fares, three month in advance fares, and six monthsfares. Target fares may be calculated based on market segment. Themarket segment includes any one of tour operator, customer type,internet bookings, holiday travelers and frequent flyers.

[0611] Generating PAX target and target fares for each POS for each O&D,compartment and month is based on the maximized network revenue is doneon a time period level. The time period level includes any one of daily,weekly, and monthly. Generating PAX target and target fares takes intoaccount market segments (i.e., customer type, frequent flyer, touroperators, internet bookings, holiday travelers). PAX target and faresmay be generated at a single travel agent level, and/or at a salesexecutive/supervisor level. Targets may be generated based on a flightlevel (i.e., an itinerary level). The linear optimization may also takeseasonality into account, may balance inbound to outbound traffic.Industry travel demand may also be excluded from the optimization step.Sensitivity analysis may be performed to determine fares at whichrejected demand should be accepted.

[0612] Additionally, in one embodiment, network revenue is unaffected byacceptance of rejected demand. Results of sensitivity analysis may bedisplayed, including rejected demand and minimum average fare foraccepting the rejected demand.

[0613] 9.17 Deriving a Model

[0614] A brief discussion of Linear Programming techniques is givenbelow. Generally, there are five steps in formulating Linear Programmingmodels:

[0615] 1. Understand the problem.

[0616] 2. Identify the decision variables.

[0617] 3. State the objective function as a linear combination of thedecision variables.

[0618] 4. State the constraints as linear combinations of the decisionvariables.

[0619] 5. Identify any upper or lower bounds on the decision variables.

[0620]FIG. 37 shows an example of the steps of an Linear ProgrammingOptimization Derivation. In so doing, one should understand the problem,the objective, and the constraints involved (step 3701).

[0621] Constructing an Analytical Model (step 3702): this step involvesthe “translation” of the problem into precise mathematical language inorder to make calculations and comparison of the outcomes underdifferent possible scenarios.

[0622] Finding a Valid Optimal Solution (step 3703): a proper solvingtechnique is chosen, depending on the specific characteristics of themodel. After the model is solved, validation of the obtained resultsmust be done in order to avoid an unrealistic solution.

[0623] In revenue planning, the optimization process can be considered acore process, or engine. As it is complex to find out which traffic mixwill be beneficial for the airline under given capacity, demand, andexpected average fare constraints, a scientific method is employed to doit. Operations research techniques deal with the problem of determiningan optimal solution with given constraints. These operations researchtechniques are will suited to the Revenue Planning Process.

[0624] As shown in the I-P-O diagram of FIG. 38, inputs 3801 to theOptimization Process 105 are market potential, average yield andscheduled capacity. With the help of linear programming techniques, theOptimization Process 105 produces its output 3802, an optimal trafficmix that expected to generate the maximum revenue. Market potential isbased on POS-O&D-Compartment—Travel month basis, yield is based onPOS-O&D-Compartment-Travel Month basis, and capacity is based onLeg-Compartment-Travel Month basis.

[0625] 9.18 Linear Programming

[0626] Linear Programming is a mathematical procedure for determiningoptimal allocation of scarce resources. In this particular LinearProgramming problem, two classes of objects are considered: first, alimited resource such as capacity and demand, and, second, an activity,such as “maximizing revenue”.

[0627] The General Form of an Optimization Problem is as follows: MAX(or MIN):

[0628] g(X₁, X₂, . . . , X_(n))

[0629] Subject to:

[0630] f₁(X₁, X₂, . . . , X_(n))≦b₁

[0631] f_(k)(X₁, X₂, . . . , X_(n))≧b_(k)

[0632] f_(m)(X₁, X₂, . . . , X_(n))=b₁

[0633] If all the functions in an optimization are linear, the problemis a Linear Programming problem.

[0634] The General Form of a Linear Programming Problem is as follows:MAX (or MIN):

[0635] c₁X₁+c₂X₂+ . . . +c_(n)X_(n)

[0636] Subject to:

[0637] c₁₁X₁+c₁₂X₂+ . . . +c_(1n)X_(n)≦b₁

[0638] c_(k1)X₁+c_(k1)X₂+ . . . +c_(kn)X_(n)≧b_(k)

[0639] c_(m1)X₁+c_(m2)X₂+ . . . +c_(mn)X_(n)=b_(m)

[0640] The General Form of the General Optimization Model is:

[0641] Max or Min g(x)

Objective function

[0642] such that f₁(x)≦b₁∀i=1, . . . , n

Constraints

[0643] x≧0

Vector valued non negative.

[0644] When g(x), f₁(x) are linear functions—Linear Programming.

[0645] Phrased another way, a Linear Programming is a problem that canbe expressed as follows (the so-called Standard Form):

[0646] where x is the vector of variables to be solved for (in thiscase, PAX target, Target Yield vectors), A is a matrix of knowncoefficients (in this case, unity), and c (unity), and b (scheduledcapacity and estimated demand vectors) are vectors of knowncoefficients. The expression “cx” is called the objective function, andthe equations “Ax<=b” are called the constraints. The above formulae canbe translated in this case as: i=n Max z = ? PAX (aod)i * Yield (aod)iI=1 Subject to (a) PAX (aod)i < = demand (aod)i for i = 1 to n i=n (b) ?PAX (aod)i < = Leg Capacity j where J = 1 to m i=1 (m = no. of Legs innetwork) (c ) PAX (aod)i > 0 PAX (aod)i = Passenger from POS “a” forroute Origin “o” and Destination “d” Yield (aod)i = Yield for POS “a”for route Origin “o” and Destination “d”

[0647] This is applied to individual F, J, Y compartments and differenttravel months. Equation (a) is the set of demand constraints andEquation (b) is the set of capacity constraints. These equations areapplied for all possible Areas of Sale and O&D combinations, and allnetwork Leg capacities. In the capacity constraint equations, all O&Dstraversing through those particular Legs are considered.

[0648] For example, to write the capacity constraint equation for LHRDXBLeg, all passengers from LHR to various Destinations belonging todifferent Points of Sale should be considered in this equation: i=n ?PAX (aod)i < = LHRDXB capacity -- Capacity Constraint for LHRDXB Leg i=1

[0649] Where i=number of possible combinations of POS-O&D

[0650] Here, passengers from UK for LHRDXB, LHRBOM, LHRMEL, etc.,passengers from USA for LHRBOM, LHRDEL, LHRDXB, etc., passengers fromCanada for LHRBOM, LHRMAA, etc., are all considered for all possibleArea of Sale and O&D combinations.

[0651] Equation (c) ensures that optimal PAX and yield cannot havenegative values. Final solutions for PAX and Yield, will be the PAXtarget and target yield for that POS for the specified O&D, Compartmentand travel month combinations.

[0652]FIG. 39 shows an Optimal Curve for a Linear Programming solution.Linear Programming model solutions will always achieve the feasibleoptimal solutions as shown in the above graph. In one embodiment, theRPS 100 uses LINDO software for the optimization processes and the RPS100 provides the input parameters as per the format required for LINDO,and LINDO output is read and shown as targets.

[0653]FIG. 40 shows an Linear Programming Optimization Model Tree. Asshown in FIG. 40, a Linear Programming model 4001 tries to achieveoptimal feasible solutions 4002. If constraints conflict with eachother, it will have no feasible solutions (4003). If a feasible solution4002 exists, LINDO will try to achieve an optimal solution 4004.Unbounded solutions 4005 exist when there is no limit on the solutions,i.e., variables can attain the value of infinity. This cannot exist inthis case, since PAX target cannot be infinity due to capacityconstraints. Hence, LINDO always gives feasible optimal solutions 4004.

[0654] Additionally, the linear optimization model of the presentinvention is particularly suitable to maximizing revenue for the entirenetwork (for example, for an airline that operates as a hub-and-spokesystem), rather than merely for a particular leg, or route.

[0655] 9.19 Optimizer Equations Example

[0656] Revenue Targets (Pax, Average Fare, Revenue) are the outputs ofthe Linear Programming model 4001. A sample network is illustrated inFIG. 41.

[0657] In this example, Leg Capacities are as follows: Leg Seats LONDXB100 DXBBOM 75 DXBKHI 75

[0658] Demand is as follows: Sector Demand Yield LONBOM 120 90 LONDXB 5060 LONKHI 60 85 DXBBOM 50 55 DXBKHI 40 50

[0659] It is necessary to maximize the network revenue for the abovesample network, based on the capacity and demand constraints. Thisproblem can be formulated as a standard linear programming problem, asshown below:

[0660] MAXIMISE

[0661] 90*LONBOM+60*LONDXB+85*LONKHI+55*DXBBOM+50*DXBKHI

[0662] SUBJECT TO CONSTRAINTS

[0663] Demand constraints

[0664] LONBOM<=120

[0665] LONDXB<=50

[0666] LONKHI<=60

[0667] DXBBOM<=50

[0668] DXBKHI<=40

[0669] Capacity constraints

[0670] LONBOM+LONDXB+LONKHI<=100

[0671] LONBOM+DXBBOM<=75

[0672] LONKHI+DXBKHI<=75

[0673] Any standard linear programming software, e.g., LINDO, can beused to solve this problem. The results are as follows:

[0674] 1. Maximum Network Revenue=12,375

[0675] 2. Optimal targets: SECTOR TARGET LONBOM 25 LONDXB 40 LONKHI 35DXBBOM 50 DXBKHI 40

[0676] 3. Excess demand: EXCESS SECTOR DEMAND LONBOM 95 LONDXB 10 LONKHI25 DXBBOM 0 DXBKHI 0

[0677] 9.20 Seasonality

[0678] There are some markets where the sale of the seat from aparticular POS might in theory optimize network revenue, but makes nocommercial sense. One example of this is seasonality-driven travel.Europe—Dubai—Australia leisure traffic, for instance, shows a heavydemand from Europe to Australia in December (and low demand fromAustralia to Europe), and the reverse in January. In other words, theremay be a long lag for a particular passenger between his Europe toAustralia flight, and his return. In addition, there is business-driventraffic that needs to be considered.

[0679] For purposes of this example, assume that for one passenger, theDecember Europe—Dubai—Australia leisure ticket is $500, and the JanuaryAustralia—Dubai—Europe return ticket is also $500 (for a total of $1,000round trip). Also, assume that there is a second passenger willing tofly one-way from Dubai to Australia in December for $700. Normally, theoptimization process would treat each such one-way flight as a separateentity, and give the result that the optimum solution is selling the$700 ticket. This, of course, would result in a net network “loss” of$300. The way to avoid this loss is to reserve a certain percentage ofseats for such “seasonal” traffic.

[0680] Another use of seasonality factors is to adjust for unusualevents that should be discounted in long-term planning. Examples of suchunusual events include wars, SARS, the Sep. 11, 2001 terrorist attacks,etc.

[0681] 9.21 Alignment of Sales and Revenue Objectives

[0682] The present invention allows alignment of sales objectives andrevenue objectives. Typically, the sales department of an airline setsits sales targets, and the revenue department sets its revenue targets.There is a built-in conflict between the sales side and the revenueside, because, conventionally, the sales targets do not take intoaccount network-level revenue, but only POS-wise revenue. In the presentinvention, the POS sales targets are linked to the revenue targets forthe POS and for the network. By setting the targets for each POS in linewith the network-level revenue objectives, the sales department can haveconfidence that their targets are aligned with the revenue targets. Thetargets for each POS are set at a month, O&D and compartment level(rather than merely overall total revenue).

[0683] For instance, consider the London—Dubai—Manila route, and theLondon—Dubai—Australia route. The London POS may be told that it cannotsell any London—Dubai—Manila tickets, because the London—Dubai—Australiais more optimal at the network level. In other words from a networkperspective, filling the London—Dubai—Manila seats and leaving theDubai—Australia seats empty is suboptimal.

[0684] In theory, a particular POS can try to sell its “rejected demand”(i.e., the demand that a particular POS is not allowed to sell, here,London—Dubai—Manila, because at the network level, there is “better use”for that demand) for a higher fare. At some point, the fare becomes highenough so as to make up for the fact that the Dubai—Australia seat isnot filled. As a practical matter, however, it is rare that the fare canbe made high enough, given prevailing market conditions and competitionin the airline industry. Thus, the salesperson at the London POS isdiscouraged (by the sales target setting process) from “chasing” theLondon—Dubai—Manila sales, because it is suboptimal from a networkrevenue perspective. The salesperson will not have targets for theLondon—Dubai—Manila route, because sales targets are set with networkoptimization in mind.

[0685] The RPS 100 also can take into account the one-way nature of sometravel. For example, in some areas of the world, there is job-relatedtravel, where the passenger might not return for a considerable periodof time (e.g., over a year). In that case, it might be optimal to“protect” the available capacity (or a portion of it) for round-trippassengers (i.e., reserve a portion of capacity), rather than allow aPOS to sell the one-way demand. (See also discussion of Core Marketsbelow, where the user has the option to specify how much demand isreserved.)

[0686] Additionally, consider the case where a passenger from, forexample, Cairo, wants to fly Cairo—Dubai—Australia and pay the same fareas the London—Dubai—Australia passenger. From a pure overall revenueperspective, the RPS 100 is indifferent to which passenger gets theticket (since the fares are the same). However, in this case, becausethe London POS had a sales target set for the London—Dubai—Australiaroute, and the Cairo POS did not, the London—Dubai—Australia passengerwill get preference for the resource allocation (rather than selling thedemand on a first come, first serve basis), so that the Londonsalesperson can meet his targets. This, of course, only applies if thereis zero impact on network revenue—if the Cairo passenger is willing topay a higher fare (i.e., there is an overriding revenue consideration),then the Cairo passenger will get preference.

[0687] If it is decided that there is, in fact, unexpected demand on theCairo—Dubai—Australia route (which was not anticipated when setting theoriginal targets), the targets for the next month may be adjustedaccordingly.

[0688] In sum, revenue targeting principles are incorporated into thesales targeting process. In turn, sales targets influence real-timerevenue decisions (e.g., re-forecasting on a daily or weekly or monthlybasis). Long term targets can also be adjusted (e.g., six month targets,one year targets).

[0689] Note also that in one embodiment, the targets are set on amonthly basis, but they can also be set on a weekly or daily basis (orany other time period, e.g., bimonthly), if needed.

[0690] 9.22 Special Handling for “Industry Travel” Demand

[0691] The “Industry Travel” demand (i.e., corporate travel by airlineemployees, travel by employees' relatives, travel by employees of otherairlines at heavy discounts due to inter-airline agreements, etc.), eventhough by its nature has a lower yield compared to other revenue demand,often cannot be avoided due to corporate requirements. To avoid thelinear programming optimizer rejecting this traffic, modifications aredone in the Linear Programming Optimization equations to ensure thatthis demand is accepted in the revenue plan.

[0692] 9.23 Balancing of Inbound/Outbound Traffic

[0693] The Linear Programming Optimizer (LPO) handles the data one monthat a time and is not sensitive to accepting the returning traffic, ifthe outbound traffic has been accepted the previous month.

[0694] The Linear Programming Optimization could also possibly accepthigh number of return traffic when the outbound traffic has beenrejected the previous month. A special module in the Linear ProgrammingOptimization specifies a minimum and maximum percentage of returntraffic that should be accepted for selected POS and O&D pairs. (Seealso discussion above regarding one-way and seasonal travel.)

[0695] 9.24 Sensitivity Analysis

[0696] Altering the input parameters can change optimal solutions, i.e.,changing the passenger demands and yield variations in the inputparameter generates different optimal solutions. Sensitivity analysis isthe term applied to the process of addressing this issue. LINDO's LinearProgramming solution report provides supplemental information that isuseful in Sensitivity Analysis.

[0697] In this case, if the RPS 100 rejects demand from any POS for anyRoute, it gives the acceptable limit of yield values, so that rejecteddemand can be accepted and it can give new optimal solutions. Hence, inorder to accept the rejected demand, input yield values should beincreased. For example, fares can be changed, to see if the rejecteddemand is now accepted.

[0698] The report layout of FIG. 42A gives the Rejected demand report ofa Point of Sale. This report gives concise details of rejected demands.It shows the travel month on the ‘X’ axis and each O&D, where the demandhas been rejected, on the ‘Y’ axis. This report can be generated forindividual compartments as well as at a total (aggregate) level.

[0699] The rejected demand report of FIG. 42A gives details of thoserejected demands (compartment wise) that the optimization process hasrejected completely. The user can enter the rejected demand value sothat report will show those O&Ds where the rejected demand is greaterthan that of the parameter value. It also can display the proposedincrease in the fare that will allow the rejected demand to be accepted.

[0700] The RPS 100 can also generate network-wise and region-wise andPOS-wise data for a selected Travel month or for all months.

[0701] Demand details where the optimization process has partiallyrejected the demand can also be displayed. It also gives the proposedfare increase so that the partially rejected demand can be accepted.

[0702] In one embodiment, functional areas of Demand Estimation 103 andRe-forecasting 102 are automated, with manual exception detection andoverride facility. This eliminates the burden of manual analysis, whichthe users would have otherwise been forced to carry out before theybegin setting the targets.

[0703] 9.25 Summary of Optimization Process

[0704] The Optimization Process 105 will be summarized using FIG. 42B.As shown in FIG. 42B, the linear optimization process 4206 takes asinputs, for example, estimated PAX demand 4201, capacity constraints4202, and estimated fares 4203. Seasonality factors 4204 and time periodchoice (e.g., weekly monthly etc.) may also be used as inputs. Thelinear optimization 4206 then goes through a process of balancinginbound and outbound traffic (4207). Industry demand may then beexcluded (4208). Target PAX is calculated (4210) and target fares arecalculated (4209). Market segment information (4216) may be used in PAXtarget calculation (4210) and target fare calculations (4209). Themarket segment 4216 may be, for example, tour operator 4211, holidaytraveler 4212, frequent flyer 4213, internet bookings 4214, and customertype (e.g., child, adult, etc.) 4215.

[0705] There are a number of target fares that may be calculated by thetarget fare calculation step 4209. For example, these may be returnfares 4218, one way fares 4219, excursion fares 4220, three months inadvance fares 4217, and six months in advance fares 4221. The varioustarget fares may be fed into a sensitivity analysis step 4222. Theoutput of the sensitivity analysis step may be displayed (4226),rejected demand may be displayed (4227) (see also discussion belowregarding rejected demand), and minimum acceptable fares may also bedisplayed (4228).

[0706] 10.0 Pre-Optimization Processes

[0707] Before carrying out the Optimization Process 105, a series ofprocesses may be run to prepare the RPS database 207 for theoptimization. FIG. 43 shows the pre-optimization process 4300. Thepre-optimization process 4300, as shown in FIG. 43 includes proratefactor generation 4301, sector route leg link generation 4302, notraffic sector nullification 4303 and a bookkeeping rate update 4304.These four processes are discussed below.

[0708] 10.1 Prorate Factor Generation 4301

[0709] Prorate factor generation 4301 is used for deriving sector yieldand revenue generation 4401 based on the prorate factor existing in theIATA prorate manual. Prorate factors for individual segments areretrieved, and, upon running this process, all operating segmentsprorate factors will be synchronized with the RPS 100.

[0710] 10.2 Sector-Route-Leg Link Generation 4302

[0711] Sector-Route-Leg combination is used to split the O&D passengersacross different route and leg and route and sector levels. This is doneto facilitate setting the passenger load on each segment and route.

[0712] 10.3 No Traffic Sector Nullification 4303

[0713] If any no-traffic rights sectors are present in the network,demand of these sectors should be set to zero before the optimization.Otherwise, no traffic sector demand can replace the demand for O&Dswhich traverse these segments. This process sets to zero the demand ofno traffic sectors, if any.

[0714] 10.4 Book Keeping Rate Update 4304

[0715] Local currency to base currency (e.g., to AED, or to U.S.dollars) conversion is done with help of an exchange rate converter inthe CVIEW 201/Planning System 204. This facility is provided to selectthe exchange rate that should be used for currency conversion in yieldand revenue calculation. Once the book-keeping month is selected withthis process, corresponding exchange rate will be used to calculate thecurrency conversion.

[0716] 11.0 Post Optimization Processes

[0717] Subsequent to the Optimization Process 105, certain processes arerun to derive the data at different levels, i.e., segment, segment androute, and leg levels (see FIG. 44). As shown in FIG. 44, thepost-optimization process 4400 includes the subprocesses of SectorRevenue Generation 4401, Leg Seat Factor Generation 4402, Sector RouteRevenue Generation 4403, and POS Revenue Variance Generation 4404. Thesefour processes are discussed further below.

[0718] 11.1 Sector Revenue Generation 4401

[0719] This process converts the POS-O&D-Comp-travel month data (Targetand Actual) for revenue, yield, PAX into POS-Sector-Comp-travel month.The sector level revenue, yield, PAX data are used in various MISreports as discussed below.

[0720] 11.2 Leg Seat Factor Generation 4402

[0721] The Leg Seat Factor (i.e., the percentage of capacity used)translates the generated sector level data into leg level data. Segmentlevel passenger data is converted to leg level, mainly to have acomparison of seat factors existing in different routes after targeting.

[0722] 11.3 Sector-Route Revenue Generation 4403

[0723] Once the sector level data is generated, it is apportioned intodifferent sector and route combinations, e.g., DXBSIN data isapportioned into DXBSIN of DXB-SIN-MEL, DXB-SIN-SYD and DXB-CMB-SIN-JKTroutes. This is done primarily to perform a comparative study ondifferent routes.

[0724] 11.4 POS Revenue Variance Generation 4404

[0725] This process makes the Revenue, PAX, Yield variance of Targetwith Actual at POS summarized level for each compartment in differenttravel months.

[0726] 12.0 Management Information System

[0727] MIS reports are generated for the needs of the management atdifferent levels. Information is categorized to meet the requirements ofTop Management/Commercial Department/Online/Offline StationManagers/(Yield Management) (see user levels illustrated in FIG. 45). Asshown in FIG. 45, the RPS 100 information hierarchy can includeCommercial Top Management 4501, Yield Management 4502, CAMS 4503,Pricing 4504, Finance 4505 and Area Managers 4506.

[0728] 12.1 Reports

[0729] Subsequent to the optimization, various sub-processes may be runto the data required for report generation.

[0730] 12.1.1 Revenue Plan Report

[0731] The Revenue Plan Report gives the POS-wise revenue plan in termsof actual, demand, and PAX target for each O&D-Comp-Travel monthcombinations with applicable booking class. It has both a preview and anExcel option (see FIG. 46). As shown in FIG. 46, the Revenue Plan Reportcan include demand and yield information for a particular region. Thenumbers for the actual PAX, PAX demand, PAX target, fares and bookingclasses are shown in this report.

[0732] 12.1.2 Fully Rejected Demand Report

[0733] The Fully Rejected Demand Report of FIG. 47 gives the detailsabout the rejected demand (compartment-wise) that the optimizationprocess has rejected completely. It is a parameterized report, where theuser can give the rejected demand value, so that the report will begenerated for O&Ds where rejected demand is greater than the parametervalue. It also gives the proposed increase in the fare to accept therejected demand. It can generate network-wise and region-wise andPOS-wise data for selected travel month or for all months. It has both apreview and an Excel Option.

[0734] 12.1.3 Partially Accepted Demand Report

[0735] The Partially Accepted Demand Report shown in FIG. 48 gives thedemand details where optimization has rejected the demand partially. Italso gives the proposed increase in fares so that partially rejecteddemand can be accepted.

[0736] 12.1.4 Commercial Target Report

[0737] The Commercial Target Report is designed to show the comparisonof actual/target details about PAX, yield and revenue in a simpleconvenient place. Percentage change of target with regard to actual isalso given. This report can be generated based on region summary,area-wise summary, and detailed level. An example of such a report isshown in the screen shots of FIGS. 49AA-49AB (which should be viewed asa single figure). As shown in FIGS. 49AA-49AB, Actual, Target andvariance numbers for PAX, yield and revenue data are shown in the tablefor four regions: Europe/North America, GCC/Yemen/Iran, MiddleEast/Africa and WAPR (West Asia/Pacific Rim). Totals (summary of thefour regions) are also shown. The two graphs on the right provide abreakdown by compartment and by region.

[0738]FIG. 49B shows a Regional Report for Europe and North Americaonly. As shown in FIG. 49B, revenue, PAX and yield can be broken down bycompartment (see tables on left). The data can also be presented ingraphical form, historical revenue data can be shown, and monthlyrevenue distribution can be shown (see right half of the figure).

[0739]FIG. 49C is similar to FIGS. 49AA-49AB, and illustrates a networkparameter summary, including revenue, PAX and average fare (yield),broken down by compartment, and by actual, target and variance data inthe tables on the left. The graphs on the right illustrate breakdown ofthe revenue by component, revenue trends, region-wise revenue breakdown,and seat factor growth.

[0740]FIGS. 49D-49E (which should be viewed as a single figure) is anillustration of the Commercial Target Report in E-dialogue. Note inFIGS. 49D-49E that certain items on the grid are specially marked. Forexample, the April target for LHRDXB shows a flag, which indicates adisagreed item. Similarly, the April target for DXBLHR is underlined.FIGS. 50A-50B (which should be viewed as a single figure), showadditional details of the Commercial Target Report. By bringing thecursor to those grid items, and “right clicking” on those items, apop-up menu comes up (see screenshot in FIGS. 51A-51B), and thecalculation details behind the numbers may be viewed. For example, thepopup menu for the April target for LHRDXB is shown in the cell for thatitem. As may be seen more clearly in FIGS. 51A-51B (which should beviewed as a single figure), the various parameters and growth factorsfor that particular cell are displayed, for example, WPGF=3%, TMS=10%,CTG=13%, CGF=5%, and EGF=13%.

12.1.5 Commercial Target Report—Outstation

[0741] The Commercial Target Report—Outstation, shown in FIGS. 52A-52B(which should be viewed as a single figure) is designed in view of AreaManagers' perspective. The report is similar to the Commercial TargetReport of FIG. 49D-49E. It also has the facility to generate either inAED or local currency. A parameter is given to display the O&Ds whichconstitute x % of total revenue. Hence, Area managers can select theO&Ds that represent 80% of total revenue, instead of showing all O&Dswith less significant revenue importance.

[0742] 12.1.6 O&D Capacity Comparison Report

[0743] The O&D Capacity Comparison Report shown in FIG. 53 gives thecapacity comparison for O&D and Compartment and Travel Monthcombinations. It helps in demand estimation process where one can lookinto the capacity growth and fine-tune the expected demand. For example,as shown in FIG. 53, for each compartment, at each compartment, for thebudget years 2002-2003 and 2001-2202, the variance and percentage isshown. In the center and right half of the figure, monthly numbers areshown. The report also has the Excel generation option.

[0744] 12.1.7 Sector Yield Report

[0745] The Sector Yield Report shown in FIG. 54 gives the PAX, yield,and revenue comparison between Target and Actual for individual sectors.A report can be generated at individual compartment level (includingtotal) and selected travel month or full year. It can also be generatedto Excel.

[0746] 12.1.8 Leg Seat Factor Report

[0747] The route-wise Leg Seat Factor Report shown in FIG. 54 shows howthe O&D PAX target is distributed among different legs. Comparison ofTarget Leg Seat Factor (SF) and Actual Leg Seat Factor (SF) is done.This helps in identifying the exceptional legs where targeted seatfactor is unusually high or low.

[0748] 12.1.9 Quick Target Report

[0749] The Quick Target Report shown in FIG. 55 gives the target figuresin one page for all months. This convenient layout facilitates the userto see his/her target in one place. It can be output to Excel, and makesit easy to share information among different users.

[0750] 12.1.10 POS Revenue Variance Report

[0751] The POS Revenue Variance Report shown in FIG. 56 gives the Targetrevenue vs. Actual revenue variation for different POS for differentcompartments. This helps in carrying out revenue analysis of differentPOS. Excel generation is also enabled for this report. FIGS. 57-58 showthe variance matrix in graphical form.

[0752] 12.1.11 Route-wise Yield and SF report

[0753] The Route-wise Yield and SF report shown in FIGS. 59A-59B (whichshould be viewed as a single figure) gives the route-wise PAX, Revenue,RPKM, ASKM, SF. A comparison is made between Target and Actual data.

[0754] 12.1.12 Core Market Strategy Report

[0755] The Core Market Strategy Report gives the marketing strategy thatshould be adopted by the individual POS for different routes indifferent months. (Here, “core markets” refers to the routes thatgenerate X % of the network revenue, for example, 80%.) It indicateswhether an airline should proceed on value basis or volume basis, and isa ready reference for Area managers to adopt a particular businessstrategy. (Here, “value basis” refers to high demand periods, wherethere is no need for discounting to fill the seats and full fares can becharged, while “volume basis” refers to low demand periods, wherewithout discounting, the seats are unlikely to be filled. (Reports suchas those shown in FIGS. 60A-64A assist with the fare setting process.FIGS. 60A-60B (which should be viewed as a single figure) show afrequency distribution of fares in graphical form (in this graph, inAED). FIGS. 61-64A illustrate fare type details for a single Point ofSale.) The Core Market Strategy Report also includes a facility togenerate the report in Excel, and to identify each O&D pair as being avolume based strategy pair or a value based strategy pair.

[0756] The core market strategy selection process is summarized withrespect to FIG. 64B. As shown in FIG. 64B, network route demand isidentified (6401). Currency value of the routes is identified (6402).Value based or volume based strategy is selected for each route (6403).Route that account for a certain percentage of network revenue areselected (6404). These routes may then be displayed and color coded(6405). The route may be displayed on the map (6406) or in a hub andspoke format (6407).

[0757] 12.1.13 Revenue Plan Progress Report

[0758] The Revenue Plan Progress Report gives the monthly comparison ofRevenue, Leg PAX demand, Capacity, Sector PAX yield, RPKM, ASKM, SF,Yield/RPKM at network level. As the month progresses, the actual columnwill be updated with flown data. FIG. 65A shows an example of a monthlyrevenue plan progress report. The report give the actual revenue bymonth, target revenue by month, the variance. The report also gives thesame monthly numbers for the leg passenger, the capacity, and the sectorPAX yield. A weekly version of the report may also be generated by theRPS 100.

[0759]FIGS. 65B-65G show examples of monthly distribution reports thatcan be generated using the RPS 100. In each of these figures, a table onthe left shows Actual, Target and Variance numbers, and a graph on theright shows the monthly data in graphical form. The figures show monthlydistribution of revenue (FIG. 65B), monthly distribution of leg PAX(FIG. 65C), monthly distribution of sector average fare (yield) (FIG.65D), monthly distribution of leg seat factor (SF) (FIG. 65E), monthlydistribution of seat factor (FIG. 65F), monthly distribution of yield inrevenue per kilometer (FIG. 65G).

[0760] 12.1.14 Threats/Opportunities

[0761] The RPS 100 may also include a facility to tabulate the variousthreats and opportunities to the revenue plan. For example, threats mayinclude a possible outbreak of a war, excessively high targets due tocapacity increase on a certain route, or competitors increasing thefrequency of flights on a certain route or reducing prices.Opportunities may include such factors as favorable conditions—forexample, favorable market conditions, a change in strategy for peakmonths (for example, focus on summer and winter, focus on particularroutes in especially lucrative markets, etc.), or withdrawal of aparticular competitor from a route.

[0762] 13.0 Additional Enhancements

[0763] The Revenue Planning System has additional enhancements which aredescribed below.

[0764] Automation: Re-forecasting and Demand Estimation may becompletely automated to reduce manual effort.

[0765] Exception Reports: there are reports to give the Re-forecastingand Demand exceptions, where manual intervention is called for. Afacility is given for correcting these Re-forecasting/demand datamanually.

[0766] Re-forecast Capping: after calculating the Re-forecasting datafor individual POS-O&D-Comp-Travel Month combinations, it is broken downto Leg level and sum of the Leg forecast is checked against the LegCapacity, and Re-forecast data is adjusted to meet the Capacityconstraints. This can reduce the forecast errors considerably,especially in the case of early booking markets, i.e., UK (Southern),Germany, etc.

[0767] Weekly skewing: weekly targets are derived based on theseasonality instead of uniformly splitting from monthly targets.Seasonality is calculated based on the current year actual flown data.

[0768] Historical Base Change: in order to exclude the 9/11 effects ontravel, the historical base is shifted to 2000 for September, October,November travel months for considering the POS materialization rates inRe-forecast PAX calculation.

[0769] Point of Sale Summary Report: this report highlights theCorporate, Region, and Area of Sale commercial objectives.

[0770] Market Share Report: it gives the target market share for a POSin different routes for each travel month. This can be generated fordifferent compartments.

[0771] 13.1 Core and New Markets

[0772] The present invention also provides a system and method ofsegregating demand targets, and includes identifying network routedemand, identifying currency value of the network route demand, anddeciding whether a POS should adopt a volume based or a value basedstrategy. The present invention also provides a system and method fordisplaying routes of the network and color coding them based on theselected strategy (see, e.g., discussion of Spider Web below). Theroutes may be superimposed on a map. The routes may be shown as a huband spoke diagram. Only routes of the network that account for at leastX % of total network revenue (i.e., “core markets”) could be displayed,if desired. The network may be a hub and spoke network, or a point topoint network.

[0773] A Core/New Markets' Entry Form is shown in FIG. 66. This is afacility to enter Core/New Markets for specified POS and Regions. Thefields are as follows:

[0774] Region—List of valid/existing regions available for selection.“ALL” can be selected.

[0775] Point of Sale—List of valid/existing point of sales available forselection. The list must be restricted to the region selected. (“ALL”can be selected.) “ALL” must be selected if Region=“ALL”

[0776] Core Markets—Text Field for user input of Core Market Share.

[0777] New Markets—Text Field for user input of New Market Share.

[0778] Capacity Growth—Text Field for user input of New Market Share.(Can be Null.)

[0779] Buttons—

[0780] Save—Saves the current record.

[0781] Clear—Clears the screen.

[0782] If changes are made, user must be prompted before clearing.

[0783] Print Preview—Prints a preview of the report shown in 1.1.2.Report must be grouped by Region. Facility to print “ALL” regions” mustbe available.

[0784] Delete—Deletes the record. If changes are made, user must beprompted before deletion.

[0785] Excel—Prints output to Excel.

[0786] Exit—Exits from the screen.

[0787] 13.2 POS Summary Report

[0788] As shown in FIG. 67, fields in this report are as follows:

[0789] Region—List of valid/existing regions available for selection.“ALL” can be selected.

[0790] Point of Sale—List of valid/existing point of sales available forselection. List must be restricted to the region selected. “ALL” can beselected. “ALL” must be selected if Region=“ALL.” For POS withterritory, the POS itself must be available in the POS list. E.g.—“UK”.This applies to all outstation reports.

[0791] Buttons:

[0792] Preview—Prints a preview of the report.

[0793] Generate to Word—Generates information in the form of a Worddocument.

[0794] Clear—Clears the screen. If changes are made, user must beprompted before clearing.

[0795] Exit—Exits from the screen.

[0796] 13.2.1 Overview of POS Summary

[0797] 1) All variances to be computed for local currency in the report.

[0798] 2) Compute growth for POS at F, J, Y and Total compartment levelsand display figures.

[0799] Calculations (in Local Currency)

[0800] Revenue Growth=[(Target Revenue−Actual Revenue)/ActualRevenue]*100.

[0801] PAX Growth=[(PAX target−Actual PAX)/Actual PAX]*100.

[0802] Yield Growth=[(Target Yield−Actual Yield)/Actual Yield]*100.

[0803] 13.2.2 Station Objectives

[0804] Core/New Markets are user entries from the entry form. (See alsodiscussion in sections 14.3-14.4 relating to the Spider Web.)Revenue/Yield variance of the top several O&Ds are displayed, at aPOS-O&D level. Here,

[0805] % Incr. in Rev. Target=[(Target Rev.−Actual Rev.)/ActualRev.]*100.

[0806] % Incr. in yield Target=[(Target yield−Actual yield)/Actualyield]*100.

[0807] 14.0 Target Pack

[0808] The target setting process (106-107 in FIG. 1) may be startedduring September of the current financial year. At that point, the flowndata may be available in CVIEW 201 only up to the month of August.Forward Booking Data will be available for the next six months(September to February) for any snapshot date in August. Hence, thereneeds to be a mechanism in place to derive the estimated flown PAXinformation for the months where flown information is not available, orwhere the month is a future month yet to be flown. The Re-forecastingprocess derives this estimated flown information (or forecasts) forthese months.

[0809] Once the targets are finalized, a set of information resourcespertaining to revenue target and business strategy (the target pack 205)may be sent across to each Area of Sale 206 in electronic form (see FIG.2). At the same time these are updated in other commercial systems, suchas CVIEW 201, etc. Discussed below are the information resourcesincluded in the target pack 205.

[0810] 14.1 Commercial Target Outstations Report

[0811] This report, shown in FIG. 68 gives the F/J/Y/Total target (PAX,Yield, Revenue) in Local Currency for each O&D and each travel months,and is a ready reference for this particular Area of Sale. It also givesa comparison between target and actual figures (actual refers to theactual flown till the month where actual data is available, forremaining months, it is Re-forecast figures). Routes may be sorted inthe high to low target revenue order.

[0812] 14.2 Station Summary Report

[0813] This report shown in FIG. 69, gives highlights of revenue planpertaining to the Point of Sale (i.e., a single station). It also givesthe Network, Regional, and Point of Sale objectives for the budget year.

[0814] 14.3 Core Market Strategy Report

[0815] The Core Market Strategy Report shown in FIG. 70 gives thestrategy that should be adopted in different markets in each month. Thestrategy is based on either volume or value.

[0816] 14.4 Spider Web

[0817] The Spider Web report (see FIG. 71) gives a graphicalrepresentation of routes/expected demand in each month in budget year.This report facilitates the area of sale in identifying the individualroutes demand well in advance. The Spider Web is prepared for inboundand outbound traffic demand. The Spider Web shown in FIG. 71 is ahub-and-spoke representation, and may use color to designate thedifferent types of routes. Alternatively, the Spider Web may besuperimposed onto a map, as shown in FIG. 72 (in the black-and-whiteprintouts of FIGS. 71-72, color is shown by using different shading).

[0818] 14.5 Route Demand Report

[0819] The Route Demand Report shown in FIG. 73 displays the demand onvarious routes in color coding. The High Demand is represented with aRed Bar, Medium Demand with a Blue and Low Demand with a Green Bar(shading is used in the black/white version of FIG. 74).

[0820] 14.6 Connection Reports

[0821]FIG. 68, discussed above, shows the outbound connection detailsfor UK POS Outbound flights. This report gives the connecting flightdetails, in terms of Day of Week connection times at hub for outboundand inbound flights originating and terminating at individual POS. Thereport acts as a ready reference for the sales department to know theconnecting flight details. FIG. 74 shows a similar Inbound ConnectionReport.

[0822] 15.0 Additional Features of Revenue Plan

[0823] (a) Decentralized Demand Estimation: FIG. 75 shows how thetargeting process 106-107 can be decentralized to have stations inputtheir demand estimation. Each POS can feed their demand by taking intoconsideration of local facts (competition, trend, business growth,economy growth, currency potential, type of traffic, popular fare etc.)and comparison can be made against the Yield Management-generated demandestimation. Decisions can be made whether to retain the station demandor not after a review. A web-enabled interface enables the Points ofSale to feed their demand. If any demand is rejected from any POS duringthe optimization, the rejected POS will be informed about the details ofother POS who captured their portion of demand. Hence, a competitive PAXdemand and yield from each POS can be expected.

[0824] (b) Frequency: Targets are typically set four-five months beforethe start of a Financial year. In order to reflect real dynamism andmarket fluctuations during current financial year, targets are revisedtwo months prior to the start of every quarter.

[0825] (c) Granularity: Targeting is done for O&D-Compartment-Travelmonth combinations. It does not address the type of fare basis thatneeds to be concentrated nor which date/DOW should have differenttargets compared to normal trend (this can be due to the type ofconnections exist, special events, other competitor's pricing strategydepend on the DOW, etc.). Hence, targeting need to be done for farebasis or Class or RBD/Date or Date range combinations. Also it will givewhat should be group (IT/Ad-hoc) vs. individual compositions that eachPOS should have.

[0826] (d) Market Segment: in one embodiment, there is no distributionof targets among different market segments. Targets may be split amongdifferent segments including frequent flyers.

[0827] (e) Optimization: An objective function of the Revenue Plan is“maximizing the revenue,” which gives the traffic mix for maximumnetwork revenue. The objective function of revenue plan can be modifiedas “maximizing net revenue”. Net revenue is revenue—cost (e.g., cateringcost). Hence, the output of this objective function will be the trafficmix with maximum net revenue.

[0828] (f) POS forecasting: A detailed methodology is used in POSforecasting by taking into consideration of seasonality andsplit-history philosophy.

[0829] (g) Target Road Map: A Road Map for each POS details the numberof bookings that it should hold at each Snapshots in order to achievethe target, so that POS can have track on the booking activity and planaccordingly.

[0830] 16.0 Advantages of the Invention

[0831] The present invention provides a number of advantages. Forexample, tangible revenue gains, arising from working the commercialorganization to a revenue plan that has been scientifically optimized toensure maximum profitability, can be realized. Pro-active identificationof core and new markets that need to be targeted can be performed.Pro-active identification of class-wise growth required for each marketcan also be performed, allowing marketing activities to be tailored tothe projected geographic and customer segmentation. Inbound and Outboundtraffic demand analytics (Spider Web) on a month to month basis can beprovided. A one-stop shop analytical tool is provided for monitoringperformance of points of sale against their targets, with drilldown/drill through facilities across business dimensions, can also beprovided. On a strategic level, the present invention enhancescollaboration between an airline's sales force and revenue optimizationdepartments by providing a shared vision in the form of an agreedrevenue plan.

[0832] It will be apparent to one of ordinary skill in the art thatalthough the present invention has been described primarily in terms ofthe airline industry, it is equally applicable to hotel and car-rentalindustries, energy, natural gas pipelines, broadcasting, shipping,sports, entertainment facilities, manufacturing, equipment leasing andcargo industries, or any industry that has limited short-term capacityflexibility and variable demand.

[0833] 17.0 Conclusion

[0834] While various embodiments of the present invention have beendescribed above, it should be understood that they have been presentedby way of example, and not limitation. It will be apparent to personsskilled in the relevant art that various changes in form and detail canbe made therein without departing from the spirit and scope of theinvention.

[0835] The present invention has been described above with the aid offunctional building blocks and method steps illustrating the performanceof specified functions and relationships thereof. The boundaries ofthese functional building blocks and method steps have been arbitrarilydefined herein for the convenience of the description. Alternateboundaries can be defined so long as the specified functions andrelationships thereof are appropriately performed. Also, the order ofmethod steps may be rearranged. Any such alternate boundaries are thuswithin the scope and spirit of the claimed invention. One skilled in theart will recognize that these functional building blocks can beimplemented by discrete components, application specific integratedcircuits, processors executing appropriate software and the like or anycombination thereof. Thus, the breadth and scope of the presentinvention should not be limited by any of the above-described exemplaryembodiments, but should be defined only in accordance with the followingclaims and their equivalents.

What is claimed is:
 1. A system for estimating airline demandcomprising: means for accessing capacity data for a previous N years ata Point of Sale level, time period level and an Origin and Destinationlevel; means for accessing flown data for a previous M years at thePoint of Sale level, time period level, and Origin and Destinationlevel; means for accessing capacity data for a forecasting period thatextends beyond a time when reservation information is available; meansfor calculating at least one of actual growth and market growth; meansfor deriving an effective growth based on the flown data, the capacitydata for the previous N years, the capacity data for the forecastingperiod and the at least one of the actual growth and the market growth;and means for generating a passenger demand forecast for a budget yearbased on the effective growth.
 2. The system of claim 1, wherein thetime period level is any of daily, weekly, or monthly.
 3. The system ofclaim 1, wherein the capacity data includes compartment level data. 4.The system of claim 1, wherein the flown data includes compartment leveldata.
 5. The system of claim 1, further comprising means for applying aset of weighting factors to the flown data and market data to derive theat least one of actual growth and market growth.
 6. The system of claim5, wherein the weighting factors include seasonality factors.
 7. Thesystem of claim 1, wherein: the means for deriving comprises means forcomparing the capacity data for the previous N years to budget yearcapacity; and its generating means comprises means for generating apassenger demand forecast for a budget year.
 8. The system of claim 1,wherein N=M.
 9. The system of claim 1, wherein N=1.
 10. The system ofclaim 1, further comprising: means for estimating average fares for thebudget year, wherein the means for deriving an effective growth uses theaverage fares to derive the effective growth.
 11. The system of claim 1,wherein the forecasting period extends beyond about twelve months.
 12. Asystem for estimating airline demand comprising: means for accessingfirst capacity data for a previous N years at a time period level andOrigin and Destination level; means for accessing flown data for aprevious M years at a Point of Sale level, time period level and Originand Destination level; means for accessing second capacity data for aforecasting period that extends beyond a time for which airlineschedules are available; means for generating an effective growth for abudget year based on the first and second capacity data; means forcalculating at least one of an actual growth and a market growth; andmeans for deriving a passenger demand forecast based on the effectivegrowth, the flown data and any of actual growth, market growth and totalmarket demand.
 13. The system of claim 12, wherein N=M.
 14. The systemof claim 12, wherein N=1.
 15. The system of claim 12, wherein M=1. 16.The system of claim 12, further including means for estimating averagefares for the budget year, wherein the means for deriving an effectivegrowth uses the average fares to derive the effective growth.
 17. Asystem for estimating airline fares comprising: means for accessingaverage fares for a previous N years at time period level, Point of Salelevel and Origin and Destination level; means for deriving an effectivegrowth based on the average fares; and means for using the effectivegrowth to generate fares forecast for a next budget year.
 18. The systemof claim 17, wherein the time period level is any of daily, weekly, ormonthly.
 19. A system for estimating airline demand comprising: meansfor accessing capacity data for a previous N years at Origin andDestination level; means for accessing flown data for a previous M yearsat a Point of Sale level and Origin and Destination level; means foraccessing capacity data for a forecasting period that extends beyond atime when reservation information is available; means for deriving anactual growth factor based on seasonality; means for deriving aneffective growth factor based on the flown data, the capacity data, theactual growth factor, the flown data and market data; and means forgenerating a passenger demand forecast for a budget year based on theeffective growth factor.
 20. A computer program product for estimatingairline demand, the computer program product comprising a computeruseable medium having computer program logic recorded thereon forcontrolling a processor, the computer program logic comprising: computerprogram code means for accessing capacity data for a previous N years ata Point of Sale level, time period level and an Origin and Destinationlevel; computer program code means for accessing flown data for aprevious M years at the Point of Sale level, time period level, andOrigin and Destination level; computer program code means for accessingcapacity data for a forecasting period that extends beyond a time whenreservation information is available; computer program code means forcalculating at least one of actual growth and market growth; computerprogram code means for deriving an effective growth based on flown data,the capacity data for the previous N years, the capacity data for theforecasting period and the at least one of the actual growth and themarket growth; and computer program code means for generating apassenger demand forecast for a budget year based on the effectivegrowth.
 21. A computer program product for estimating airline demand,the computer program product comprising a computer useable medium havingcomputer program logic recorded thereon for controlling a processor, thecomputer program logic comprising: computer program code means foraccessing a first capacity data for a previous N years at time periodlevel and Origin and Destination level; computer program code means foraccessing flown data for a previous M years at a Point of Sale level,time period level and Origin and Destination level; computer programcode means for accessing a second capacity data for a forecasting periodthat extends beyond a time for which airline schedules are available;computer program code means for generating an effective growth for abudget year based on the first and second capacity data; computerprogram code means for calculating at least one of an actual growth andmarket growth based on the first capacity, the second capacity and theflown data; and computer program code means for deriving a passengerdemand forecast based on the effective growth, the flown data and any ofactual growth, market growth and total market demand.
 22. A computerprogram product for estimating airline fares, the computer programproduct comprising a computer useable medium having computer programlogic recorded thereon for controlling a processor, the computer programlogic comprising: computer program code means for accessing averagefares for a previous N years at time period level, Point of Sale leveland Origin and Destination level; computer program code means forderiving an effective growth based on the average fares; and computerprogram code means for using the effective growth to generate a demandfares forecast for a next budget year.
 23. A computer program productfor estimating airline demand, the computer program product comprising acomputer useable medium having computer program logic recorded thereonfor controlling a processor, the computer program logic comprising:computer program code means for accessing capacity data for a previous Nyears at Origin and Destination level; computer program code means foraccessing flown data for a previous M years at a Point of Sale level andOrigin and Destination level; computer program code means for accessingcapacity data for a forecasting period that extends beyond a time whenreservation information is available; computer program code means forderiving an actual growth factor based on seasonality; computer programcode means for deriving an effective growth factor based on the capacitydata, the actual growth factor, the flown data and market data; andcomputer program code means for generating a passenger demand forecastfor the budget year based on the effective growth factor.
 24. A methodfor estimating airline demand comprising: accessing capacity data for aprevious N years at a Point of Sale level, time period level and anOrigin and Destination level; accessing flown data for a previous Myears at the Point of Sale level, time period level, and Origin andDestination level; accessing capacity data for a forecasting period thatextends beyond a time when reservation information is available;calculating at least one of actual growth and market growth; deriving aneffective growth based on the flown data, the capacity data for theprevious N years, the capacity data for the forecasting period and theat least one of the actual growth and the market growth; and generatinga passenger demand forecast for a budget year based on the effectivegrowth.
 25. The method of claim 24, wherein the time period level is anyof daily, weekly, or monthly.
 26. The method of claim 24, wherein thecapacity data includes compartment level data.
 27. The method of claim24, wherein the flown data includes compartment level data.
 28. Themethod of claim 24, further including the step of applying a set ofweighting factors to the flown data and market data to derive the atleast one of actual growth and market growth.
 29. The method of claim24, wherein the weighting factors include seasonality factors.
 30. Themethod of claim 24, wherein the deriving step includes comparing thecapacity data for the previous N years to budget year capacity.
 31. Themethod of claim 24, wherein N=M.
 32. The method of claim 24, whereinN=1.
 33. The method of claim 24, further including the step ofestimating average fares for the budget year, wherein the effectivegrowth is derived using the average fares.
 34. A method for estimatingairline demand comprising the steps of: accessing a first capacity datafor a previous N years at time period level and Origin and Destinationlevel; accessing flown data for a previous M years at a Point of Salelevel, time period level and Origin and Destination level; accessing asecond capacity data for a forecasting period that extends beyond a timefor which airline schedules are available; calculating at least one ofan actual growth and a market growth; and generating a passenger demandforecast based on the flown data and any of actual growth, market growthand total market demand.
 35. The method of claim 34, wherein N=M. 36.The method of claim 34, wherein N=1.
 37. The method of claim 34, whereinM=1.
 38. The method of claim 34, further including the step ofestimating average fares for the budget year, wherein the effectivegrowth is derived using the average fares.
 39. A method for estimatingairline fares comprising the steps of: accessing average fares for aprevious N years at time period level, Point of Sale level and Originand Destination level; deriving an effective growth based on the averagefares; and using the effective growth to generate a demand faresforecast for a next budget year.
 40. A method for estimating airlinedemand comprising the steps of: accessing capacity data for a previous Nyears at Origin and Destination level; accessing flown data for aprevious M years at a Point of Sale level and Origin and Destinationlevel; accessing capacity data for a forecasting period that extendsbeyond twelve months; deriving an actual growth factor based onseasonality; deriving an effective growth factor based on the capacitydata, the actual growth factor, the flown data and market data; andgenerating a passenger demand forecast for a budget year based on theeffective growth factor.